Systems, methods, and program products for facilitating parcel combination

ABSTRACT

Machine learning systems and methods are described in regard to automating and acting upon evaluations of hypothetical composite project sites of 2+ disparate land parcels so as to allow a developer, owner, or other stakeholder to see and act upon potential land uses that are not reflected in conventional valuations. Some variants include a feature augmentation protocol for speciating one or more detailed structures feasible for development, a pattern matching protocol for identifying viable composite project sites that might suit a developer&#39;s requirements, technologies for accommodating latent preferences, proactive virtual development of co-owned disparate parcels, a notification protocol implementing offers to numerous potential sellers whose responses might affect project viability, wise virtual development and prioritization, or other such innovative configurations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates land parcels and potential project sites according toone or more improved technologies.

FIG. 2 illustrates a displayable model comprising a development ofseveral virtual shelters or other building models in which one or moreimproved technologies may be incorporated.

FIG. 3 depicts an international implementation featuring special-purposetransistor-based circuitry in which one or more improved technologiesmay be incorporated on one or both sides of the border.

FIG. 4 depicts a distributed ledger in which one or more improvedtechnologies may be incorporated.

FIG. 5 depicts data-handling media containing particular informationitems in which one or more improved technologies may be incorporated.

FIG. 6 illustrates a displayable top-view model comprising a developmentof a virtual building model that spans several parcels in which one ormore improved technologies may be incorporated.

FIG. 7 illustrates a digital map progression in which one or moreimproved technologies may be incorporated.

FIG. 8 depicts a system comprising a network-connected client deviceimplementing various implementation protocols and other content in whichone or more improved technologies may be incorporated.

FIG. 9 illustrates a progression through several stages of developmentin which one or more improved technologies may be incorporated.

FIG. 10 depicts a map image depicting several Toronto postal codes inwhich one or more improved technologies may be incorporated on a displayor other non-transitory data storage medium.

FIG. 11 depicts a laterally translated map image like that of FIG. 10but in which one or more improved technologies may be incorporated forchanging a map magnification or position (or both).

FIG. 12 depicts a plot of project site (candidate) positions againstvarious types of potential project sizes in which one or more improvedtechnologies may be incorporated.

FIG. 13 depicts another plot of potential project site positions withadditional ranges and options in which one or more improved technologiesmay be incorporated.

FIG. 14 depicts a digital object development in which one or moreimproved technologies may be incorporated.

FIG. 15 depicts a user carrying a network-connected mobile device inwhich one or more improved technologies may be incorporated.

FIG. 16 depicts an offer-descriptive message generated according to oneor more improved technologies.

FIG. 17 depicts a client device in which one or more improvedtechnologies may be incorporated.

FIG. 18 depicts a server in which one or more improved technologies maybe incorporated.

FIG. 19 depicts prioritizations and a map image in a network in whichone or more improved technologies may be incorporated.

FIG. 20 depicts a plot of a direction against various types of potentialproject sites in which one or more improved technologies may beincorporated.

FIG. 21 depicts an operational flow in which one or more improvedtechnologies may be incorporated.

FIG. 22 depicts another operational flow in which one or more improvedtechnologies may be incorporated.

FIG. 23 depicts yet another operational flow in which one or moreimproved technologies may be incorporated.

FIG. 24 depicts still another operational flow in which one or moreimproved technologies may be incorporated.

FIG. 25 depicts a particular scenario and progressive data flow in whichclient devices interact with one or more servers according to one ormore improved technologies.

FIG. 26 depicts another particular scenario and progressive data flow inwhich client devices interact with one or more servers according to oneor more improved technologies.

FIG. 27 depicts another scenario and progressive data flow in whichclient devices interact with one or more servers according to one ormore improved technologies.

DETAILED DESCRIPTION

The detailed description that follows is represented largely in terms ofprocesses and symbolic representations of operations by conventionalcomputer components, including a processor, memory storage devices forthe processor, connected display devices and input devices. Furthermore,some of these processes and operations may utilize conventional computercomponents in a heterogeneous distributed computing environment,including remote file servers, computer servers and memory storagedevices.

It is intended that the terminology used in the description presentedbelow be interpreted in its broadest reasonable manner, even though itis being used in conjunction with a detailed description of certainexample embodiments. Although certain terms may be emphasized below, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such.

The phrases “in one embodiment,” “in various embodiments,” “in someembodiments,” and the like are used repeatedly. Such phrases do notnecessarily refer to the same embodiment. The terms “comprising,”“having,” and “including” are synonymous, unless the context dictatesotherwise.

“Above,” “accelerating,” “achieved,” “aggregate,” “any,”“application-type,” “application-specific,” “assembled,” “augmented,”“automatic,” “availability,” “based on,” “because,” “complete,”“composite,” “comprising,” “conditional,” “consensus-driven,”“configured,” “correlated,” “current,” “decelerating,” “decreasing,”“digital,” “directly,” “displayable,” “distributed,” “executed,”“facilitating,” “first,” “forward,” “geographic,” “given,” “higher,”“hybrid,” “implemented,” “in combination with,” “included,” “indicated,”“inductive,” “inferred,” “integrated,” “later,” “matching,” “moderate,”“more,” “mutually,” “multiple,” “negatively,” “not including,”“numerous,” “of,” “otherwise,” “owned,” “parcel-selective,”“particular,” “partly,” “positively,” “prioritized,” “private,”“public,” “real-time,” “received,” “remote,” “rendered,”“requester-specified,” “responsive,” “scoring,” “second,” “seeding,”“sequencing,” “shorter,” “signaling,” “significant,” “simultaneous,”“single,” “smart,” “so as,” “spanning,” “special-purpose,” “specific,”“subtle,” “suitability,” “techniques,” “temporal,” “third,” “through,”“transistor-based,” “undue,” “unobtrusive,” “updated,” “upon,”“utility,” “very,” “via,” “virtual,” “within,” “without,” or other suchdescriptors herein are used in their normal yes-or-no sense, not merelyas terms of degree, unless context dictates otherwise. As used herein“data transformative” instruction sets are those that primarilyimplement other kinds of computations. Although one of these types ofinstruction sets may invoke the other as a subroutine, only very rarelyis a single code component of instructions a true hybrid. As used hereina computer response to a user action is not “automatic” if the responsemerely implements the user action. But computer responses to a useraction may be “automatic” if the user-intended event ultimately triggersthe computer response in a complex cascading or combinational way thatfew users could foresee without access to advanced technologiesdisclosed herein. Two numbers are “within an order of magnitude” or “onthe order of” one another if they differ by a factor of ten or less.

In light of the present disclosure those skilled in the art willunderstand from context what is meant by “remote” and by other suchpositional descriptors used herein. Likewise they will understand whatis meant by “partly based” or other such descriptions of dependentcomputational variables/signals. A set of items is “numerous” if atleast two dozen items are included. A set of items is “very numerous” ifat least two hundred items are included. A responsive event is a “realtime” result if a last defined trigger thereof (e.g. a user action orother programmatically detected condition) occurred less than 30 secondsbefore the real-time responsive result. As used herein one or morephysical locations “correspond” with a map, prioritization, development,presentation, or condition thereof if the former refers to the latter,if the latter refers to the former, or if one or more digital objects(e.g. records) establish a linkage between the two. Such correspondencemay or may not be one-to-one in respective variants. A “prioritization”is a distinguishing indication tending to cause one or more options1984D to become more favorable relative to one or more other optionssuch as by demoting or disqualifying the other option(s). An indicationis “distinguishing” as used herein if it creates or clarifies any suchnew (apparent or other) favoritism. An item is “within” a range as usedherein if it is not outside the range.

As used herein a depiction is “rendered” if its shape or a 2-dimensionalprojection thereof is shown graphically, portrayed as a pixel-based orvector-based list of shape objects, or otherwise expressedcomprehensively enough for a human to perceive an impression of itsshape. As used herein items that are “particular” or “subject” or“associated” are distinct from other items not so described in a givenscenario. These are not otherwise imbued with substantive weight herein,unlike such terms as “primary” or “higher.” The term “other” and ordinalidentifiers like “first” are likewise used to distinguish items fromother items, not for signaling a temporal or other substantive sequence.

Terms like “processor,” “center,” “unit,” “computer,” or other suchdescriptors herein are used in their normal sense, in reference to aninanimate structure. Such terms do not include any people, irrespectiveof their location or employment or other association with the thingdescribed, unless context dictates otherwise. “For” is not used toarticulate a mere intended purpose in phrases like “circuitry for” or“instruction for,” moreover, but is used normally, in descriptivelyidentifying special purpose software or structures.

Reference is now made in detail to the description of the embodiments asillustrated in the drawings. While embodiments are described inconnection with the drawings and related descriptions, there is nointent to limit the scope to the embodiments disclosed herein. On thecontrary, the intent is to cover all alternatives, modifications andequivalents. In alternate embodiments, additional devices, orcombinations of illustrated devices, may be added to, or combined,without limiting the scope to the embodiments disclosed herein.

FIG. 1 illustrates an overhead view of a geographic region 111A in whicha reference land parcel 160A may be combined with a first adjacentparcel 161A to form a contiguous and potentially viable first projectsite 121A, with a second adjacent parcel 162A and perhaps a third parcel163A to form a second (option for a) project site 122A, or with both toform various larger assemblages. A largest superset assemblage projectsite 123A that includes these assemblages also includes parcels 160B,163A, 164 as shown into a nominally contiguous parcel group. Notably, insome contexts a maximized-assemblage-type project site 123A isrecognizable by one or more machine learning modules described herein asunsuitable for further expansion (e.g. by virtue of no additionaladjacent parcels being eligible to add to the assemblage according toavailable parcel description records). In others, however, a projectsite may combine land parcels into a non-contiguous project site.

As used herein, a plain reference numeral (e.g. like 123) may refergenerally to a member of a class of items (e.g. like computing devices)exemplified with a hybrid numeral (e.g. like 123A) and it will beunderstood that every item identified with a hybrid numeral is also anexemplar of the class. Moreover although a reference numeral sharedbetween figures refers to the same item, most figures depict respectiveembodiments.

FIG. 2 shows one or more storage or display media 204 containing one ormore instances of species 201A, of pointers 203, of depictions 297A-B,or of portions 293 thereof. Such depictions 297A may comprise a top viewof a geographic map as a digital image 296A, for example, in which acomposite site 123 (shown with diagonal shading) is augmented with a(virtual) model 202A of a viable shelter (e.g. a retail store) or othercode- and specification-compliant structure. For each speciationprotocol and project site one or more corresponding models 202A may beincrementally developed as a mode of feature augmentation furtherdescribed below. The direction of such computation can be altered,moreover, such as by an activation of one or more controls 225A-B thatenlarge the selected composite site 123 (e.g. by adding a parcel 160A,161A); by an activation of one or more controls 225C that reduce theselected composite site 123 (by removing one or more parcels); or by anactivation of one or more controls 225 that modify the speciationprotocol or its parameters.

FIG. 3 shows one or more instances of systems 300 by which a device userin one region 11 (e.g. in Ontario, Calif.) may interact virtually withmany parcels in another region 22 (e.g. New York, USA). Records 314pertaining to such parcels may establish one or more associations 367,dimensions 368, types 370, valuations 380, or other quantifications 369thereof. Such types 370 may establish codes 371, ownership, userestrictions, or other attribute definitions pertaining to each parcelor portion thereof. Alternatively or additionally, quantifications 369(e.g. of transaction histories 381 of a subject parcel or that ofcomparable parcels 382) or attributes of current structures or featuresthereof may affect parcel or site valuations 380. Moreover in some casesusers in respective regions 111B-C may collaborate so that a local agentmay observe a subject parcel visually, for example, to facilitatesame-day evaluation by remote device users buying or selling interestsin the parcel. Records 314 pertaining to a parcel may not be online, forexample, necessitating time-sensitive investigation by a local agent.

Whichever of these regions 111 contains the parcels 160-164 beinganalyzed corresponds to one or more local site maps (e.g. with one ormore coordinates or dimensions 368), one or more municipal or otherregional zone types 370 (e.g. defined by one or more parcel useconstraint codes 371 or other regulatory definitions 372), one or morevaluations 380 (e.g. affected by an ownership history or propertytransaction comparables 382), or other such information as describedherein.

FIG. 3 also depicts special-purpose transistor-based circuitry 330optionally implemented as an application specific integrated circuit(ASIC) or in a user interface (UI) governance server, for example—inwhich some or all of the functional modules described herein may beimplemented. Transistor-based circuitry 330 includes one or moreinstances of various modules 331-338 as further described below.Interface modules 331, for example, each including an electrical nodeset 341 upon which informational data is represented digitally as acorresponding voltage configuration 351. Alternatively or additionally,transistor-based circuitry 330 may likewise include instances of controlmodules 332 (e.g. configured to invoke one or more other modules) eachincluding an electrical node set 342 upon which informational data isrepresented digitally as a corresponding voltage configuration 352.Transistor-based circuitry 330 may likewise include instances ofspeciation modules 333 each including an electrical node set 343 uponwhich informational data is represented digitally as a correspondingvoltage configuration 353. Transistor-based circuitry 330 may likewiseinclude instances of authorization modules 334 each including anelectrical node set 344 upon which informational data is representeddigitally as a corresponding voltage configuration 354. Transistor-basedcircuitry 330 may likewise include instances of indexing modules 335each including an electrical node set 345 upon which informational datais represented digitally as a corresponding voltage configuration 355.Transistor-based circuitry 330 may likewise include instances ofresponse modules 336 each including an electrical node set 346 uponwhich informational data is represented digitally as a correspondingvoltage configuration 356. Transistor-based circuitry 330 may likewiseinclude instances of recognition/learning modules 337 each including anelectrical node set 347 upon which informational data is representeddigitally as a corresponding voltage configuration 357. Andtransistor-based circuitry 330 may likewise include instances oftransmission modules 338 each including an electrical node set 348 uponwhich informational data is represented digitally as a correspondingvoltage configuration 358. In some variants, as described below, suchmodules implement such functionality jointly (e.g. in conjunction withother modules or processors described herein). Alternatively oradditionally, in some variants such modules (or components thereof) maybe geographically distributed across one or more networks 350.

FIG. 4 shows one or more data-handling media 404. Operational data 405thereon may include one or more instances of records 414, ofparcel-specific data 406, of project site data 407, of preference data,or of acquisition data. For example such parcel-specific data 406 mayinclude one or more instances of postal codes 441, of identifiers of adistrict 443, of identifiers of a region 444, of identifiers of a city444, of reference parcel identifiers 448A, of parcel identifiers 448Bpertaining to parcels suitable for inclusion with a correspondingreference parcel for a project/site under consideration, of coordinates(e.g. pertaining to a lot boundary), or of other such geographic data440. Alternatively or additionally such parcel-specific data 406 mayinclude one or more instances of owner names 451; of correspondingstreet or email addresses 453, of dates 455 (e.g. of a recordedretrieval, transmitted offer, or other recorded event as describedherein), of explicitly selected or other apparent preferences 466, or ofother such owner data 450. Alternatively or additionally suchparcel-specific data 406 may include one or more instances of public orother third-party records 314.

Likewise such project site data 407 may include one or more instances ofproject site identifiers 471, of parcel or subscriber lists 473 for eachrespective project site, of (protocols for) seeding 475 that defines howseeding is/was done for a given species or specimen, of identifiers ofprocessing protocols 476 (e.g. that define how speciation is/was donefor a given species or specimen), of reference parcel identifiers, ofassociated parcel identifiers, or of other specimen-descriptive data 470referenced herein. Alternatively or additionally such project site data407 may include one or more instances of specimen-specific objectivemachine-learning-based scores 481, of determination dates 483 or similarevaluation data provenance, of scores reflecting subjective specimencompatibilities 484 (e.g. partly based on objective indicia and partlybased on a preference profile of a potential buyer as described herein),of evaluation identifiers 486 that designate what each rank or otherscore herein signifies, of cycle counts 487 that designate how manyrecursions or other iterations of a protocol 476 were used in repeatablygenerating a given species 201A (of FIG. 2) from its correspondingseeding 475, of current ranks 488 of each species hold relative to othergenerated species 201 of the same type, or other such augmentedevaluation data 480.

In some variants such operational data 405 may likewise include one ormore instances of session or other user interaction dates 483 (e.g.signaling when an entity requested information); of preference profile(e.g. designating points or ranges of ideal project site size, estimatedprice, or profit margin in a given investor's project or campaign); ofsearch, presentation duration, or other action histories by which suchpreference profiles may be derived; or of other such expressions of(apparent) preferences 466.

In some variants such operational data may likewise include one or moreinstances of historical or proposed prices; of option exercise or otherfulfillment deadlines; of lists; of messages, of default values (e.g. adesignated value as described herein initially set by the system butavailable to change); of estimated returns on investment or otherfigures of merit; of tracked module invocations (whereby a taskdescribed herein is performed by a remote instance of one or more of theabove-described modules 331-338 as a response to a locally transmittedrequest); or of other acquisition data pertaining to a potential oractual parcel availability described herein.

As used herein a “parcel assemblage” is a (nominally) contiguous set ofreal property parcels not yet all commonly owned. Two parcels are“contiguous” if they share a property line or if their respectiveboundaries are sufficiently proximate that the two parcels arefunctionally adjacent. Two parcels can be “adjacent” across a publicstreet only if they are suitable to be linked by a bridge, tunnel, orother artificial structure. As used herein an “instantaneous” responseto a triggering event is one that is completed in less than one secondafter the triggering event. As used herein an operation is“deterministic” only if current temporal indicia and iteration-specificrandomness do not affect its outcome. As used herein a protocol or otherprocess is “deterministically repeatable” if seeding information,protocol identification, versions, and other operational data 405 ispreserved (e.g. on a public blockchain 455) with sufficient fidelity andlasting accessibility that a mutation or other digital speciationthereof that was generated before may be perfectly and systematicallyre-created. A collection of geographic parcels are referred to as“geographically dispersed” herein if more than half of the parcels ofthe collection are each separated from all of the other parcels in thecollection by more than 100 meters. As used herein a project site is“identified” by obtaining street addresses, boundary coordinates orother legal definitions 372 that provide shape and position information,alphanumeric parcel identifiers 448, or other such parcel-specific data406 describing parcels thereof.

Terms like “feature-augmentation-type” refer herein not only to featureaugmentation per se but also to other technologies in which seeding orspeciation are used for gleaning viable and detailed recommendation data(e.g. scores 481, ranks 488, merit-based default values, or other“better” configurations initially presented in lieu of other availablecounterparts) derived from one or more profile tags or other heuristics.In light of teachings herein, for example, such seeding or speciation(or both) can be gleaned from a crowdsourced or other action history soas to augment the features of the identified species without any undueexperimentation. See FIGS. 8-13.

FIG. 5 shows one or more data-handling media 504 in which a compositesite 123 has been sufficiently expanded that a larger building model202B has become feasible. An alleyway gap 509 is now spanned by themodel 202B signifying (1) that one or more land use restrictions 517have been encoded to allow the corresponding alley to be repurposed forsuch construction and (2) that the composite site suitability isoptimized by such a species 201. To the degree that such encoding reliesupon an unverified assumption that a local government might authorizesuch repurposing, a species 201 should include a suitable annotation tothat effect.

FIG. 6 shows an overhead view of a vicinity 600 in region 111A depictingunrealized improvements to potential assemblage construction sites123A-B (e.g. in augmented reality video, virtual reality video, or instill images 296). In site 123A, a single virtual building model 202B ofa best-speciated virtual building spans multiple land parcels 160-164 asshown in FIG. 1 or 6. As shown another reference parcel 160C is shownthat is of a “first” type 370, 670A. Depending upon which one or moredefinitions 372 are used for defining the first type 370, 670A, this maysignify that parcel 160C is owned by a “first” entity 610A or thatparcel 160C has another “first” defining attribute 617A (or both).Likewise in various examples one or more type definitions 372 (e.g.featuring an owner or broker entity 610B or other parcel attributes617B) establish that several parcels including a “primary” parcel 162Aand an “alternative” parcel 162B are of a “second” parcel type 370, 670Bas shown).

If a user 10 has manifested a subtle interest in a “primary” parcel162A, it is useful that a response module 336 is configured to discoverand develop one or more “best alternative” parcels 162B that have a sameassociated entity 610B (e.g. as a manager or operator) or other sametype 370, 670B with the “primary” parcel 162A. An alternative may beevaluated as “best” according to a current scoring protocol 476 (e.g.comprising an application of one or more suitable scoring functions in aprescribed sequence) assigned to or selected by the device user 10, forexample. As used herein a user's interest in a parcel is “subtle” if itis determined from the parcel being included in a composite project siteof interest without the user having actuated any control 225 thatcoincides with the parcel. Likewise a user's interest in a parcel is“very subtle” if it is gleaned merely from being of the same type 370,670 as a parcel for which the user has a subtle interest. This canoccur, for example, in a context in which a selection protocol 476Iassigned to or selected by the device user 10 is configured to identifywhich candidate parcel 162 among several is “best” by prioritizingproject sites 123 and species 201 according to evaluation data 480signaling a closest similarity to that of a site 123 that includes the“primary” parcel 162A. This can occur, for example, in a context inwhich the user would otherwise feel ambivalent about approaching acurrently-associated human entity 610B of the second-type parcels; inwhich the identity of the “primary” parcel 162A is a valuable tradesecret that would be destroyed by revealing it to the current owner; andin which processing one or more images 296 each corresponding to analternative (project site 123 that includes an alternative) second-typeparcel 162B allows the potential buyer or seller (or both) to assess asuitability 519 of each alternative second-type parcel 162B for asuccessful transaction and real-world project authorization. In somecontexts this may include using a currently preferred speciationprotocol 476B to develop a model 202C that spans each alternativesecond-type parcel 162B with one or more other parcels 163B of thecorresponding project site 123. See FIG. 27.

FIG. 7 illustrates an image progression 700 of one or more data-handlingmedia 704 in which one or more improved technologies may beincorporated. In depicting a region 111C (e.g. of the Greater Torontoarea) an earlier version 862A of a map image 296B a Type “A” projectsite 121 is selected for inclusion 788A of a spiral-shape marker 785that corresponds to (an instance of) a geographic zone 786 within region111C.

In a later version 862B of the map image 296B the same site 121 isunchanged, still indicating the inclusion 788A. Earlier version 862Alikewise depicts (an inclusion 788B of) a Type “B” project site 122Athat is omitted from the later version 862B of the map image 296B,signaling that it has been disqualified from continued inclusion.Earlier version 862A likewise depicts (inclusion 788C-D of) Type “C” and“D” project sites 123 designated for omission 778A from the laterversion 862B of map image 296B. Later version 862B likewise depicts (aninclusion 788E of) some Type “E” project sites 121-123 that were absentfrom the earlier version 862B of the map image 296B, signaling that theyhave since been prioritized.

This may occur, for example, in a context in which one or more actionstaken by a particular user 10 signal an apparent or other preference 466not to view a large number of Type A-D project sites and in which a Type“C” and “D” project sites 123 may (in some variants) include afirst-type or second-type parcel 161, 162 as described above. Suchactions may include a favorable signal 703A with regard to one or moreType “E” project sites or an unfavorable signal 703B (e.g. actuating a“thumbs down” or “close” control 225) with regard to one or more otherproject sites. Favorable signals may include actuating a “zoom in” or“select” control 225 or hovering disproportionally over a favored marker785 or zone 786 (e.g. relative to other markers 785, zones 786, or othersuch controls 225 incrementally actuated by hovering). Those skilled inthe art will recognize other favorability-indicative orunfavorability-indicative actions, context-specific or otherwise, inlight of teachings herein.

In some variants, moreover, such a speculative preference 466 may beacted upon by replacing one or more early inclusions 788F each with acorresponding substitution 789 and appropriate species reconfigurationand re-processing. This can occur, for example, in a context in which auser is (apparently) uninterested in a previously more prevalent projecttype and in which one or more project sites of interest are ripe forre-processing one or more attributes 617 of a project type that is nowdeemed favorable (e.g. Type “E”). Alternatively or additionally aconcentration of higher-priority inclusions 788E mapped within an image296 or other local region 111 may be enhanced by inserting suchinclusions 788E in response to a favorable signal 703A (e.g. a pointer787 hovering in a blank area 779A) or to high ranks 488 in newly foundinclusions 788 in or near a mapped image 296B.

Alternatively or additionally, a broader inquiry about preference 466may be implemented by causing an inclusion 788G of or substitution of anuncommon project type option as an automatic and conditional response toa determination that a new type has become available within a mappedregion. This can occur, for example, in a context in which a user hasnavigated toward a sufficiently sparsely populated area (e.g. byrequesting Postal Code M3C in Toronto) or in which one or morenon-private developments by other users triggers an automaticdevelopment 900, 1400 of such a new type within or near a mapped region.See. FIG. 11.

FIG. 8 depicts a system 800 comprising a network-connected client device1700A implementing various implementation protocols 476A-I and otherdigital content 890 in which one or more improved technologies may beincorporated. Such content 890 may include one or more instances ofoperating parameters 831A-B, of prioritizations 833, of presentations834 (e.g. maps 835), of queries 836, of classifications 838, or of othersuch indications 840. Alternatively or additionally, such content 890may include one or more instances of natural-language digitalexpressions 850, of histories 863, of preferences 866, or of profiles867 (or combinations thereof). In some variants moreover expressions 850may likewise include one or more instances of requests 851 or othernotifications 852, of values 853, of associations 854, of editing orother parametric fields 857 that may comprise labels or other user text858, of proximities 859, of coordinates 861, or of respective versions862 thereof.

FIG. 9 depicts a high-level development 900 comprising an extraction973, speciation 975, and rendering 976 according to a developmentprotocol 476H and one or more operating parameters (e.g. seeding 475 orcoordinates 861). During extraction 973 qualifying candidate sites923A-D (e.g. ones that include a reference parcel 160D) are prioritizedat a first stage 13 to form a best subset thereof at the second stage14. Parcel-specific data 406A, site data 407A, one or more applicabletypes 670C, or other such content 990 for respective candidates may beused for prioritization of candidates. At least one prevailing site 923is processed during speciation 975 to form a best subset of buildingmodels 202D-F or species at the third stage 15. At least one prevailingmodel/species is processed during rendering 976 to form one or moredepictions 297C-D to form a presentable at least one best building model202F in an inventory at stage 16.

FIG. 10 shows a geographic depiction 297E of several species 1021A,1022A, 1023A, 1024A, 1025A, 1026A of unrealized (virtual) developmentson a display or other non-transitory data handling medium 404, 504 likethose described above. They are shown in relation to one or more(instances of mapped representations of) highways, bodies of water,(land tracts defined as) postal codes, or other such real-worldfeatures. This can occur, for example, in a context in which a projectsite-specific estimated total acquisition price, model-specific arealapportionments (e.g. simultaneously presenting respective squarefootages of two or more categories displayed with suitable labels foreach), model-specific total development costs, expected return oninvestment, or other such quantifications 369 of particular merit arepresented overlapping or otherwise adjacent a perspective rendering andin which an automatically generated and ranked alternative (project site121-123 or) species 201, 1021-1026 encompassing a reference parcel 160can be indexed through with a single user action that also triggers anupdate of the quantification(s) 369 to correspond with the shownspecies. In some variants, moreover, the duration of a presentation to auser 10 is used to update a preference profile 867 of the user, asfurther described below. A pointer 787A traces a path 1089 acrossseveral mostly-blank areas 33A-C, 34A-C that each correspond to areal-world area (e.g. a postal code, neighborhood, or other geographicregion 111) potentially of particular interest to the user 10 moving thepointer 787A. Each movement, gaze, pause, or other machine-recognizableuser action may signal user preference 466, 866 in light of teachingsherein. See FIGS. 11-21. Hovering over (a marker 785 of) a species1021-1026, for example, signals an interest in a modeled sizequantification 369, map legend, or other single-site pop-up depiction(such as depictions 297C-D of FIG. 9) that becomes visible whilehovering. A longer hover or examination signals more interest—e.g. usinga threshold on the order of 20-80 milliseconds—as does zooming in on anearby image center 31. Navigation actions like panning to an adjacentarea 33, 34 may likewise be used, if such a threshold is exceeded, as anincremental favorable signal 703A of interest in whatever species1021-1026 or areas 779 are presented during such action.

FIG. 11 shows a much more magnified map as a geographic depiction 297Fthat includes a new image center 31B consistent with a user havingpanned and zoomed or otherwise having triggered a navigation so as todepict some of the postal codes 33B-C and other features earlier viewedas the depiction 297E of FIG. 10. Most of the species 1021A, 1022A,1023A, 1024A, 1025A, 1026A introduced in FIG. 10 “slid off” the map as aresult of such navigation, with only a few species 1022B, 1024B, 1026B-Cstill shown “in situ” in both depictions 297E-F following thetransition.

Depiction 297F also features several inchoate species 1027 comprisingmap-resident project sites of particular opportunity, such as where oneor more component sites are already owned or readily acquired by anaffiliate of the current device user. If the user hovers a pointer 1087over or clicks on (a marker 785 of) an inchoate species 1027 a pop-upmenu or similar control is presented by which the user is invited toconfirm one or more type-indicative, size-indicative, or other defaultparameters 2047 by which an ensuing development 900, 1400 will proceed.Alternatively or additionally a development of such a preconfigured“next option” may be triggered programmatically as processing resourcesbecome available or otherwise as described herein.

Although panning or jumping or zooming to a depiction 297F having such anew image center 31B signals a geographic preference 466, 866 asignificant amount of information may be added, removed, gleaned, andsolicited in an intuitive manner. Some variants feature a programmaticinclusion 788H partly based on a display having plenty of unused mapspace (e.g. having a net species density D below a threshold T that ison the order of 0.2 to 0.5 species per square centimeter on a display orprojection surface) and partly based on a user having selected a species1025 of the same type during the same session. Some variants feature aprogrammatic omission 778B of even an already-complete species frominventory based on a rarity of that type even with such a sparse map toavoid an expectation of such instances be processed and presented laterin the session. To satisfy such an expectation, another approach is tosupply a type-indicative directional marker 1185A—optionally with adistance-indicative scalar component—until and unless another suitablespecies 1021 of that same type falls within a user-selected region ofinterest. To satisfy a user's curiosity, an interface may be programmedto show the user a more detailed marker 1185B that includes a buildingshape or further operational controls, for example, in response to apointer 1084 hovering over a smaller marker 785, 1185A so that a userunderstands immediately what the marker means. Likewise atype-indicative directional marker 1185C for an uncommon-type speciesthat is about to appear within a user-selected region of interest may besupplied so as to allow the user to have an immediate and intuitiveopportunity to learn about that uncommon project type 370 without havingto undertake a detailed dialog or learning curve.

FIG. 12 shows a plot 1200A depicting the exact project sizes 1211 andposition 1211 of each of several project options 1284A-J of potentialinterest to a particular user during a user interaction session. Asindicated with dashed squares, a distribution of user-favored projectoptions 1284A-D and other project options 1284E-H are each plottedagainst their respective scalar sizes 1211 (e.g. as measured in squarefeet or other resources/objectives). This can result, for example, froma user clicking on various versions 862 of maps, lists, or other imagesthat are of the same type 370 as denoted by respective markers 785 in acontext in which such plots are helpful for a reader of this documentbut not needed for any user.

A rightmost column of site options are all portrayed with an iconictriangular marker 785, signifying that each is a developed species 1025of the same project type 370 as other instances of species mapped inFIGS. 7 and 10-11 or plotted in FIGS. 12-13. As shown four of thesespecies 1025 have been “favored” (such as by a user selectively optingto view a depiction or giving another favorable signal 703A thereofwhile viewing a map 835). Some of the options 1284E-F depict projecttypes 370 (e.g. each signifying a less-common category of virtualbuilding such as a museum or stadium) that are included on a map mainlyto help a user understand the context. The other six options 1284A-D,1284G-H are more common, but only four of the options 1284A-D areactually favored by the user so far. Those four options establish a sizerange 1242A and a positional range 1242B that say a lot about a user'slatent preferences, but so do nearby-plotted options 1284G-H that a userhas apparently seen but not favored. And so do within-range options thatare (visible as being) of other types 370 and apparently seen but notfavored by the user.

Such subtle indications of user preference are especially useful, forexample, in a context in which significant learning and tediousexpression would otherwise delay or prevent an actionable understandingsufficient to warrant development 900, 1400 tailored to suchpreferences. Technologies described herein may be used to glean latentand specific preferences, for example, relating to a latitude,longitude, distance from a shore or other border, or other geographicalfactors. See FIGS. 19-22. They may also be used to glean usefulpreferences pertaining to traffic, crime, population density, or otherquasi-geographical factors ‘by encoding project features into a scoringprotocol 476D by which an artificial intelligence module (e.g. a neuralnetwork) may discern which next candidate options are most worth ofdevelopment 900, 1400 in real time.

As used herein a direction may be called “forward” if it is apparentlyfavored by a user or otherwise presumably pointing toward an advantage.In some variants a “forward” direction is determined, for example, byresponding to a user's newly-favored species/project options changing(1) by establishing and testing hypotheses via testing a distributionchange 1244 against 32+ compass directions, a larger project size 1211,a smaller project size 1211, and other options as hypotheses and (2)designating one or more (apparent) primary or secondary directions 2012that seem likelier in light of the distribution change 1244.

In some variants a user's “favored” features are determined withhindsight or other nuance. In one protocol a first indexing modules 335is configured to respond to one or more user actions 1594 identifyingseveral options 1025 of a primary project type 370 by designating theprimary project type 370 as “favored.” A second indexing module 335 isinvoked to determine systematically whether a secondary project type 370is favored by triggering a development 900, 1400 of one or moreother-types options 1021-1024 within one or more ranges 1242 associatedwith a distribution 1250 comprising the several options 1025 of theprimary project type 370 and after causing a presentation 834 of atleast one option (e.g. to identify species 1021-1022) of the one or moreother-types options 1021-1024 of the other project types 370 modifyingthe presentation 834 so as to indicate that (apparently) a secondaryproject type 370 is currently preferred as a conditional response to atleast one option is (apparently) also favored.

In some variants a forward change 1244 to an established range 1242 istested for completeness by responding to (one or more user actions 1594identifying) several options 1025 of a primary project type 370 bydesignating the primary project type 370 as “favored” and inviting anexpansion of the range 1242 further forward by triggering a development900, 1400 of one or more same-type options 1025 repeatedly so long asthe more-forward options are favored repeatedly. This can occur, forexample, in a context in which such an iterative migration jumps“forward” fast enough to cross a user's forward-most preference boundaryso that further-forward options 1025 are presented vividly (e.g. withspecies in which presented depictions include building shapes) but notfavored all in a single interaction session.

Alternatively or additionally a distribution of favored options 1025 maybe systematically enriched by a scoring protocol 476D that favors anintermediate range 1242H within and spanning a midpoint of a primaryrange 1242 or largest interstitial range 1242F. This can occur, forexample, in a context in which such an enrichment may diversify favoredoptions so as to facilitate subsequent further exploration in other“forward” directions and in which a user might not otherwise feel that asession was “complete” enough in revealing compatible options within aregion, so that no offers or other actions of commitment seemappropriate until a later date.

Plot 1200A signals that no spade-shaped markers 785 are currentlyfavored, for example, nor are any Z-mirrored offsets 1205C or species1022 or any psi-shaped offsets 1205A or species. Plot 1200A also signalsinchoate species 1027 that signal map-resident project sites ofparticular opportunity, such as where one or more component sites arealready owned or readily acquired. If a user hovers a pointer 1084 overor clicks on (a marker 785 of) an inchoate species 1027 a pop-up menu orsimilar option is presented by which a user is invited to confirm one ormore type-indicative, size-indicative, or other default parameters 1247and thereby to trigger a development. Alternatively or additionally adevelopment of such a preconfigured “next option” may be triggeredprogrammatically as processing resources become available or otherwiseas described herein.

FIG. 13 shows another plot 1200B that depicts several favored projectsite/species options 1284A-D of a single type 370 as well as othersite/species options 1284E-H that are at least somewhat useful forgleaning one or more preliminary preferences 466, 866 unobtrusively.Like size range 1242A and position range 1242B of FIG. 12, a range 1242Cfairly signaling similarity to options 1284A-D is suitable for use indetermining scoring protocols 476D or otherwise implementing best “nextsteps” for identifying and presenting relevant additional options 1284i-K.

Perhaps a least relevant one of these is option 1284 i at least insofarthat (1) it seems unlikely to be of interest to someone who favorsoptions 1284A-D and (2) even if it is favored it is not very apparentwhy. It could be an anomaly, for example, signaling user error orpreference for a larger-size project or preference for a new anduncommon project type 370 that does not resemble that of species1021-1026. This ambiguity could be resolved or at least reduce by adevelopment 900 of one or more other options 1284K within or near one ormore ranges 1242A-C that circumscribe already-favored options 1284A-D.But until such other-type outlying options 1284 are favored by aless-ambiguous user action it might be said that they add little insightinto any user preferences 466, 866. Initially such options 1284 i are ofsmaller diagnostic value.

The same cannot be said as to options 1284G, 1284J that offer amoderately different position 1212 or as to options 1284H that offer amoderately different size 1211 relative to (one or more ranges 1242A-Cthat fairly) signal similarity to favored options 1284A-D. Range 1242Cis constructed by drawing circles that are each concentric with acorresponding favored option 1284A-D, including all points between oramong those circles. But it will be understood that polygons or manyother shapes that circumscribe a group of favored options will likewisefairly signal similarity to such favored options without any undueexperimentation. In some variants, moreover, such a range 1242 maycorrespond with two or more areas that are not geographicallycontiguous.

As used herein an option 1284J is “significantly” larger than a favoredrange 1242 if an expansive separation therebetween is a distance 1343A-Bmore than 10% of a positional extent of the range 1242 or more than onestandard deviation away from a mean of favored option points therein. Asused herein an option 1284J is “moderately” larger than a favored range1242 if such a separation therebetween is more than 50% and less than200% of a positional extent of the range 1242 or is 2-5 standarddeviations above a mean of favored option points therein.

As used herein an option 1284G is “significantly” smaller than a favoredrange 1242 if a reductive separation therebetween is a distance morethan 10% below a lower positional extent of the range 1242 or more thanone standard deviation below a mean of favored option points therein. Asused herein an option 1284G is “moderately” smaller than a favored range1242 if such a separation therebetween is more than 25% and less than75% smaller than a positional extent of the range 1242 or is 2-5standard deviations below a mean of favored option points therein.

As used herein an option 1284H is “significantly” different than afavored range 1242 if it is significantly larger or smaller. And as usedherein an option 1284H is “moderately” different than a favored range1242 if it is moderately larger or smaller. By whatever expression 850 asame-type option 1284 is presumptively most valuable if it is moderatelydifferent than a favored range 1242 that fairly signals similarity toother project options of the same type 370 and in a generally “forward”direction 2012 that is sparsely developed. And a new-type option ispresumptively most valuable if it is (1) within or less-than-moderatelydifferent than a favored range 1242 or (2) selectively responsive to adevelopment request that identifies a species type 370 into which aninchoate species 1027 or open area 779 should be developed. See FIGS. 9and 14.

FIG. 14 depicts a digital object development 1400 in which one or moreimproved technologies may be incorporated. Each iteration 1424A-E maysignal an incremental step of extraction 973, speciation 975, orrendering 976 or components thereof according to any of variousprotocols 476A-I. In many contexts such iterations 1424 will include oneor more depictions of a site 122B selected or otherwise configured suchthat one or more virtual structures shown on a site 122B haveuser-specific or cohort-specific suitability 519 that tends to improveover time using supervised or other machine learning technologies andthe best available preference data as described herein.

FIG. 15 shows a system 1500 in which a client device 1700B may interactwith one or more networks 350, 1550 and may store, display, or implementcomputation protocols 476 upon one or more data-handling media 1504(e.g. display screen 1512). Such media 1504 may handle one or moreinstances of recommendations 1521, of statuses 1522, or of otherresponses 1525; or of phone numbers 1557 or other elements 1559 relatedto various messages 1560. In some contexts such responses/messages mayfeature or otherwise implement one or more instances of searches 1561,of project site or building shapes 1564, or of various otherconfigurations 1572 described herein; of applications 1577; ofcomparisons 1579 (e.g. by a recognition module or other learning module337); of inquiries 1583; of options 1584; of contracts 1585; of levels1587; or of other such terms 1590 described herein. Device 1700B maylikewise include or otherwise access one or more instances of resources1591 (e.g. records), of time periods 1592, of user actions 1594 (e.g.gestures or input), of controls 1595, or of graphical images 296C.

In some variants one or more special-purpose modules 331-338 areconfigured to facilitate adducing information about one or morepreferences of a user 10 by a kind of crowdsourcing, optionally inresponse to an association 854 between the user 10 and (actions of) oneor more other users 10. For example an interface module 331 may beconfigured to trigger one or more geographically mapped or otherpresentations 834 of a first project site prioritization 833 favoringfirst-type and second-type project options 1584 (see FIG. 10) over athird-type project option 1584 (e.g. corresponding to a less-favoredspecies 1022, 1023) to a display screen 1512 of device 1700B. This canoccur, for example, in a context in which a response module 336 isconfigured to express a second project site prioritization 833automatically and conditionally favoring the third-type project option1584 over the first-type and second-type project options 1584 to thedisplay screen 1512 partly based on an explicit indication 840 (e.g.from a first indexing module 335) of a preference 866 of the user 10 ofthe client device 1700B for the third-type composite project option 1584over the second-type project option 1584 (e.g. directly from the displayscreen 1512 having dismissed or otherwise disparaged the second-typecomposite project option 1584) and partly based on an implicitindication 840 of a preference 866 (e.g. from another indexing module335) of the user 10 for the third-type composite project option 1584over the first-type project option 1584 inferred from (at least) secondand third device users 10 who have dismissed or otherwise disparaged thefirst-type composite project option 1584. In some variants, for example,such effective speculative preferential inferences can justify advancedextraction 973, speciation 975 or rendering 976 during a session or weekof (a latest one of) the user actions 1594 and in which such development1400 would otherwise either require prohibitively expensive andpervasive development 1400 to maintain or be pervasively obsolete (e.g.based upon stale operational data 405). See FIGS. 16 and 26.Alternatively or additionally such a presentation 834 may depict orotherwise correspond to one or more rendered images 296 virtuallyrepresentative of a mapped area or other local content 890, 990 (e.g.depicting a geometric shape 1564 of other-type composite project options1584).

FIG. 13 illustrates one or more data-handling media 1604 (e.g. a displaypresenting an image 296 or other memory/storage) containing anoffer-descriptive message 1560 comprising a sender identifier 1626, arecipient identifier 1627, and a series of natural-language clauses1677A-E. As shown clause 1677A includes a subject line that comprises ascalar expression 850 of parcel-specific premium price 2438 (e.g. “Wantto sell for $195,000 today?”).

Clause 1677B includes a background statement identifying (at least) apublished information source (e.g. “tax records”), a street address orother parcel identifier, and a corresponding published valuation 380(e.g. “According to Zillow, your property at 1923 Main Street is worth$176,475”). Clause 1677B also presents an offer-descriptive statementfeaturing a proposed payment amount 1645 identified by anatural-language description 1616 (e.g. “earnest money” or “optionpurchase price”), and a payment mode identifier 1617 (e.g. “wiretransfer” or “cashier's check”) intended to entice the owner (e.g. “Wewant to give you a $1000 down payment immediately, directly via Venmotoday.”)

Clause 1677C includes additional transaction terms 1590 including aproposed duration 1628 and a request 851 for a phone number 1557 orother routing element 1559 (e.g. as contact information) to facilitatethe inchoate transaction (e.g. “It may take 18 months for us to decidewhether to complete the purchase, but either way you keep the $1000.Does that sound fair? If so please reply with your phone number.”).Clause 1677D includes additional transaction terms 1590 (e.g. “Pleasenote that if you accept the $1000, you will have entered a legallybinding contract. Also please note that another seller might accept this$1000 on a similar property if you don't reply quickly. This is a ‘firstcome, first serve’ opportunity.”). Clause 1677E includes a reference tofurther transaction terms (e.g. “Detailed terms for this contract areprovided below.”). Following the salutation and signature, theprospective buying entity may self-identify with a place name 1667 localto the reference parcel or an area code 1668 local to the referenceparcel.

In some variants multi-parcel development 900, 1400 are facilitated byinvoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to cause a depiction 297 of anaugmented first species 201A of a first composite project site 123A thatcombines (at least) a first-type subject parcel 161A with a second-typeprimary parcel 162A and invoking transistor-based circuitry (e.g. one ormore instances of authorization modules 334) configured to respond to aparcel-specific first user request 851 or other first user action 1594specific to the second-type primary parcel 162A (e.g. clicking a “showowner” button or other such first action 1594 indicative of a desire todisplay such particular metadata). Such an authorization module 334 may,for example allow an indication 840 to be presented of one or moresecond-type parcels 162 that include a second-type alternative parcel162B wherein responding to the first user request 851 in reference tothe second-type primary parcel 162A comprises invoking transistor-basedcircuitry (e.g. one or more instances of speciation modules 333)configured to develop (at least) an augmented version 862 of a species201B of the second composite project site 123B that combines a virtualbuilding model 202C spanning (at least) a third-type associated parcel163B owned by a third entity 610 with the second-type alternative parcel162B owned by the second entity 610B. This can occur, for example, in acontext in which the first-type, second-type, and third-type parcels areall mutually exclusive, in which one or more virtual building models 202span two or more land parcels 160-164 of the second project site throughvery numerous iterations 1424 of refinement speciation 975 as aconditional response 1525 to a parcel- or site-selective second useraction 1594, in which a potential buyer would otherwise be forced toindicate an identity of the second-type primary parcel 162A beforeapproaching each potential seller of a desirable second-type primaryparcel 162A, and in which a sheer number of permutations of anditerations 1424 upon composite project options 1584 for plausiblesuitability 519 would otherwise render such vetted outreach much toocomputationally burdensome or counterintuitive for a potential buyer. Insome variants, for example, such effective vetting may be accomplishedby a strategic real-time response 1525 to a user selection or to one ormore implicit indications 840 of user preferences 866 (or both). SeeFIG. 27.

Referring now to FIG. 17, there is shown a client device 1700 in whichone or more technologies may be implemented. Device 1700 may include oneor more instances of processors 1702, of memory 1704, of user inputs1708, and of display screens 1712 all interconnected along with thenetwork interface 1706 via a bus 1716. One or more network interfaces1706 allow device 1700 to connect via the Internet or other networks toor within corporate or other human entities 610. Memory 1704 generallycomprises a random access memory (“RAM”), a read only memory (“ROM”),and a permanent mass storage device, such as a disk drive.

Memory 1704 may contain one or more instances of operating systems 1710,web browsers 1714, and local apps 1724. These and other softwarecomponents may be loaded from a non-transitory computer readable storagemedium 1718 into memory 1704 of the client device 1700 using a drivemechanism (not shown) associated with a non-transitory computer readablestorage medium 1718, such as a floppy disc, tape, DVD/CD-ROM drive,flash card, memory card, or the like. In some embodiments, softwarecomponents may also be loaded via the network interface 1706, ratherthan via a computer readable storage medium 1718. Special-purposecircuitry 1722 may, in some variants, include some or all of theevent-sequencing logic described herein as transistor-based circuitry354 (e.g. in a peer-to-peer implementation) and one or more securityfeatures 1760 (e.g. a fob or similar security apparatus).

In some contexts security feature 1760 may implement or otherwiseinteract with a removable or other digital wallet (e.g. as securityfeature 1760). Such wallets may (optionally) each include one or moreinstances of private keys, of utility tokens, of crypto currency, ofprovenance data, or of device-executable code snippets (e.g. smartcontracts) configured to perform one or more functions as describedbelow. In some embodiments client device 1700 may include many morecomponents than those shown in FIG. 17, but it is not necessary that allconventional components be shown in order to disclose an illustrativeembodiment.

With regard to data distillation as described herein, the selectiveinclusion of suitable project sites and viable structures depictedthereon are described herein as an example of “pattern matching.” In thecontext of artificial intelligence, pattern matching is a branch ofmachine learning in which token sequences are searched for occurrencesof (data corresponding to) a pattern. In light of technologies describedherein, advanced machine learning may allow one or more serversdescribed herein to overcome a computational barrier that previouslymade effective pattern matching in regard to structures that could bebuilt on a regional assortment of project sites computationallyprohibitive. As described herein, various technologies allow artificialintelligence to generate, sift, and selectively present vettedhypothetical structures automatically. Terms like“pattern-matching-type” refer herein not only to pattern matching per sebut also to prioritization and other technologies in which one or moresequences of tokens are searched for occurrences of suitable adjacentland parcels for assemblage analysis, ranking, or recommendation 1521(e.g. in an image 296 presented to a display screen). In a virtual oraugmented reality implementation, for example, such composite projectsites and virtual structures thereon may come into view as a device 1700approaches and enters a corresponding physical area 33, 34, 779.

Referring now to FIG. 18, there is shown an exemplary server 1800 one ormore of which may likewise be configured to perform functions asdescribed below. Server 1800 may include one or more instances ofprocessors 1802, of memory 1804, of user inputs 1808, and of displayscreens 1812 all interconnected along with the network interface 1806via a bus 1816. One or more network interfaces 1806 allow server 1800 toconnect via the Internet or other networks to or within entities asdescribed below). Memory 1804 generally comprises a random access memory(“RAM”), a read only memory (“ROM”), and a permanent mass storagedevice, such as a disk drive.

Memory 1804 may contain one or more instances of operating systems 1810,hosted websites 1814, and aggregation modules 1826. These and othersoftware components may be loaded from a non-transitory computerreadable storage medium 1818 into memory 1804 of the server 1800 using adrive mechanism (not shown) associated with a non-transitory computerreadable storage medium 1818, such as a floppy disc, tape, DVD/CD-ROMdrive, flash card, memory card, or the like. In some embodiments,software components may also be loaded via the network interface 1806,rather than via a computer readable storage medium 1818. Special-purposecircuitry 1822 may, in some variants, include some or all of theevent-sequencing logic described with reference to FIG. 3 (e.g. in apeer-to-peer implementation) and one or more security features (e.g. afirewall), not shown. In some embodiments server 1800 may include manymore components than those shown in FIG. 18, but it is not necessarythat all conventional components be shown in order to disclose anillustrative embodiment.

Referring now to FIG. 16, there is shown an exemplary network 1950 thatmay implement or be connected with one or more other devices 1700,servers 1800, or networks 350, 1550 configured to perform functions asdescribed herein. A mapped image 1096 of current status data 1922 isalso shown featuring current positions of various users 10A-D in acohort exploring a corresponding geographic region. Various types 670Cof developed species 1021B, 1022B, 1023B, 1024B, 1025B, 1026B withinthat region are also shown in relation to streets, bodies of water, orother land features (e.g. boundaries 1918). In some contexts one or morecontrols 225D accessible via pointer 787B may signify an open area 779that a user 10D may select, for example, to signal a requesteddevelopment 900 of one or more sites there. Likewise one or morecontrols 225E-F may respectively signify one or more developed sites ofrespective species 1024, 1025 that a user 10D may select, for example,to see a complete set of parcel or model identifiers 1988, shapedepictions, or other components of a description 1989. Alternatively oradditionally such controls 225E-F may signal disparagement or trigger amodified development 900 thereof.

Also an archived (earlier) prioritization 1933A is shown in which ahigher-ranked option 1984A was associated with a type of species 1024A-Bthen (apparently) preferred over one or more other options 1984Bassociated with a once-less-preferred type of species 1025A-B signaling,for example, that self-storage projects were once deemed preferable overother land use options. Likewise prioritization 1933A indicates that amoderately-ranked option 1984B is associated with an earlier type ofspecies 1025A-B apparently preferred over one or more other options1984C associated with a then-less-preferred type of species 1022A-Bsignaling, for example, that a site partly owned by a known orreasonable entity 610A was deemed preferable over a site partly owned byan unknown or unreasonable entity 610B. In this way a ranking of one ormore options 1984A-C may depend upon a quantification 369 (e.g.comprising a count or reputation score) of one or more entities 610corresponding to a site 923.

A later prioritization 1933B is likewise shown in which some options1984A-B have become less preferable relative to one or more otheroptions 1984C-D. This can occur, for example, in a context in which anevaluation protocol 476A has been applied to one or more of the options1984A-D; in which one or more quantification 369 or types 370, 670 in (adescription 1989 of) one or more of the options 1984A-D has beenadjusted and they have been re-ranked accordingly; and in which suchupdated prioritizations 1933 are presented to the user in a ranked list473. Alternatively or additionally such updated prioritizations 1933Bmay be indicated implicitly, such as by higher-ranked options 1984C-Dbeing presented in (a mapped image 1096 or other) status data 1922 thatfeatures a removal/omission 778 of another option 1984A-B that waspreviously shown. Such changed prioritizations 1933B may be tailored toa particular user 10D in some variants, ranking species 1021-1026 (atleast partly) based on a present-moment real-world distance from amobile device 1700 of the target user 10D to each ranked site thereof orbased on same-session user actions 1594 in which a pointer 787A-Ccontrolled by that user 10D hovers over or near a marker 785 of eachranked species (or both). In some variants such a scoring protocol 476D(e.g. one that assigns different incremental ranking points fordifferent kinds of interactions) can be particularly useful before auser 10D has given any explicit indications 840 that a given projectspecies, site, or type is favored. A prioritization 833, 1933 for a newguest user 10D, for example, may include a scoring protocol 476D thatplaces a positive incremental value 853 on a defined user action 1594 ata zone 786, site-specific label, or other component of a map-residentfirst marker 785, 1185. Alternatively or additionally such a scoringprotocol may place a larger incremental value 853 (e.g. 3 points) upon alater action 1594 at a first marker than (that placed) on an earlieraction 1594 (e.g. 0-1 points) at said first marker 785, 1185.

FIG. 20 shows a plot 1200C in which several sets of project options1284, 1584, 1984 of potential interest to a user are each represented ina column. A scalar expression of each site's proximity to an ideal (e.g.as a point relative to a lakeshore or user's “perfect” project size)signals a corresponding respective offset 2005A-H (e.g. aligned with aforward direction). As indicated with dashed squares, a distribution2050 of user-selected or otherwise favored project options 1025 andtheir respective offsets 2005E are shown. This can result, for example,from a user clicking on various versions 862 of maps, lists, or otherimages that are of the same type 370 as denoted by respective markers785 in a context in which the distribution 2050 is also associated withone or more instances of changes 2044 from a prior version in aprogression of distributions 2050; of means 2045, variances, or otherterms of statistical characterization; of project types 370,compatibilities 484, geographic regions, or other project attributes bywhich options/species are grouped; or of other parameters 2047 by whichsuch distributions 2050 are generated or evaluated.

A rightmost column of site options are all portrayed with an iconictriangle-type marker 785, signifying that each is a developed species1025 of the same project type 370 as other instances of species mappedin FIGS. 7 and 10-11 or plotted in FIGS. 12-13. As shown by dashedsquares three of these species 1025 have been “favored” (such as by auser giving a favorable signal 703A thereof while viewing a map) and onehas not. (Although these favored species 1025 are respectively indicatedin FIG. 20 at a plot-resident minimum 1301C, maximum 1302C, and offset2005E such favor will more often be ascertained via user actions 1594across or near one or more species 1025 presented on a map 835 oroptions 1984B shown in a list 473 instead.) A range 1242E of size,positional, or other project-specific offsets 2005 of scalar values 853is accordingly said to “span” the favored species 1025, the rangeincluding a minimum 1301C and a maximum 1302C as shown. This can occur,for example, in a context in which the user has not overtly selected anysuch favored type 370, size 1211, or other self-descriptive preferenceparameter 2047 during a current access session except by interactingwith the several favored species as shown but in which (at least adistribution 2050 of) a user's latently-preferred direction 2012 hasbeen inferred by such interaction; in which the user has had along-enough opportunity (e.g. exceeding a threshold within an order ofmagnitude of 1-2 minutes) to view the unfavored same-type species 1025plotted on the same map 835 as the others; in which an extension range1242G spans a basic range minimum 1301C to a closest unfavored offset2005H; in which a range 1242E that includes the several favored speciesis extended toward the unfavored offset 2005H so as to include some orall of extension range 1242; in which the resulting larger-than-basicrange is thereafter used as a parameter 2047 in a scoring protocol 476Ddescribed herein; and in which at least a closest unfavored species 1025thereby augments the distribution 2050 of favored species 1025.

In some variants a “forward” direction is determined, for example, byresponding to a user's newly-favored species/project options changing(1) by establishing and testing hypotheses via testing a distributionchange 2044 against 32+ compass directions, a larger project size 1211,a smaller project size 1211, and other options as hypotheses and (2)designating one or more (apparent) primary or secondary directions 2012that seem likelier in light of the newly-favored species/projectoptions.

In some variants a user's “favored” features are determined withhindsight or other nuance. In one protocol a first indexing modules 335is configured to respond to one or more user actions 1594 identifyingseveral options 1025 of a primary project type 370 by designating theprimary project type 370 as “favored.” A second indexing module 335 isinvoked to determine systematically whether a secondary project type 370is favored by triggering a development 900, 1400 of one or moreother-types options 1021-1024 within one or more ranges 1242 associatedwith a distribution 1250 comprising the several options 1025 of theprimary project type 370 and after causing a presentation 834 of atleast one option (e.g. associated with species 1021-1022) of the one ormore other-types options (e.g. associated with species 1021-1024) of theother project types 370 modifying the presentation 834 so as to indicatethat (apparently) a secondary project type 370 is currently preferred asa conditional response to at least one option 1021-1022 (apparently)also being favored.

In some variants a forward change 2044 to an established range 1242 istested for completeness by responding to (one or more user actions 1594identifying) several options 1025 of a primary project type 370 bydesignating the primary project type 370 as “favored” and inviting anexpansion of the range 1242 further forward by triggering a development900, 1400 of one or more same-type options 1025 repeatedly so long asthe more-forward options are favored repeatedly. This can occur, forexample, in a context in which such an iterative migration jumps“forward” fast enough to cross a user's forward-most preference boundaryso that further-forward options 1025 are presented vividly (i.e. withspecies in which presented depictions include building shapes) but notfavored all in a single interaction session and in which discovering adevice user's unconscious preference for a more forward project sitewould otherwise entail a non-intuitive and more intrusive expenditure ofsession time and other computing resources and thereby prevent manygreat multi-parcel development opportunities from ever being examined.

Alternatively or additionally a distribution of favored options 1025 maybe systematically enriched by a scoring protocol 476D that favors anintermediate range 1242H within and spanning a midpoint of a primaryrange 1242 or largest interstitial range 1242F. This can occur, forexample, in a context in which such an enrichment may diversify favoredoptions so as to facilitate subsequent further exploration in other“forward” directions and in which a user might not otherwise feel that asession was “complete” enough in revealing compatible options within aregion, so that no offers or other actions of commitment seemappropriate until a later date.

Plot 1200C signals that no spade-shaped markers 785 are currentlyfavored, for example, nor are any Z-mirrored offsets 2005C or species1022 or any psi-shaped offsets 2005A or species. Plot 1200C also signalsinchoate species 1027 that signal map-resident project sites ofparticular opportunity, such as where one or more component sites arealready owned or readily acquired. If a user hovers a pointer 1084 overor otherwise interacts with (a map-resident marker 785 of) an inchoatespecies 1027 a pop-up menu or similar option is presented by which auser is invited to confirm one or more type-indicative, size-indicative,or other default parameters 2047 and thereby to trigger a development.Alternatively or additionally a development of such a preconfigured“next option” may be triggered programmatically as processing resourcesbecome available or otherwise as described herein.

In some variants a machine learning method (see FIGS. 21-27 as describedbelow) accelerates, guides, or otherwise facilitatescomputer-implemented development 900, 1400. A device operated by a user10A is allowed to receive a first graphical presentation 834 of a firstplurality of primary project options 1284, 1584, 1984. They areindicated on a first map 835 and at least some are directly or otherwisefavorably designated by (one or more actions 1594 of) the user 10A. Aposition 1212 of a first (actually, currently, or otherwise) favoredoption 1284A of the primary project options is more forward in somehumanly explainable direction 2012 than a position of a second option1284B (e.g. at minimum 1301B) of the primary project options and so thata position of a third option 1284C (e.g. at maximum 1302B) of theprimary project options is more forward than the position of the firstoption 1284A.

Certain user actions 1594 signal a user's stronger interest in moreforward positions such as a favorable indication 840 in regard to aforward-most primary project option 1284B, a less-than-favorableindication 840 (e.g. a demotion in or removal from a “favorites” list473) in regard to a rearward-most primary project option 1284C, apointer movement path 1089 in a user-favored direction 2012, or anavigation in a forward direction 2012. One or more such user actions1594 in a direction 2012 toward an area that has no same-type or othersuitable primary project options triggers, in some variants, indexingand response modules 335, 336 to implement a real-time development 900,1400 in which a scoring protocol 476D is optimized at a position 1303Bfurther forward than a range 1242B-C that includes at least the firstand third options 1284A, 1284C.

Those skilled in the art will recognize what user actions 1594 signalfavor and can generally agree upon a convenient polygon, ellipse, orother range shape 1564 flows from the currently favored options toexclude severely divergent options 1284 selectively. As a conditionalresponse 1525 to one or more user actions 1594 that signal a forward netchange 2044 from a “before” distribution 2050 to an “after” distribution2050 of favored options, the indexing module 335 selects adapts ascoring protocol 476D to be optimized at a position 1303B moderatelyfurther forward than the “before” range 1242. In some contexts the newposition is offset further forward by a distance by a distance 1343 thatis substantially proportional to (i.e. within a factor of 2) or at leastwithin an order of magnitude of the forward net change 2044. This canoccur, for example, in a context in which the forward net change 2044 iscomputed as a difference between the respective means 2045 of ormidpoints of the before and after distributions 2050, in which the type370 of the species 1025 developed at the new position may be the same asthat of the favored primary project options 1284, and in which themethod would otherwise need an unwieldy questionnaire or other slowinteraction to glean a user's preferences so that vast numbers ofiterations 1424 are not wasted on developing irrelevant or other unwisenew project options 1284.

In some variants a second instance of indexing and response modules 335,336 is configured to evaluate candidate positions using a scoringprotocol 476D that is optimized further forward than a range 1242 thatincludes favored primary project options 1284. This can occur, forexample, in a context in which the new species 1023B is of a second type370 that is included in a new area 779B to alleviate a situation inwhich the user's actions 1594 (e.g. of navigations from map/depiction297E to map/depiction 297F) would otherwise cause a disappearance of (alast species 1023A of) that second type 370 or in which the user'saction(s) 1594 would otherwise necessitate one or moredirection-indicative markers 1185A-C pointing toward an off-map species1023A to avoid such a disappearance (or both). In the context of FIG.11, for example, the inclusion 1023B of a new species 1023B as areal-time response 1594 to a user's zoom-in action to a depiction 297Fthat avoids any need to include a type-indicative directional marker1185 pointing to an off-map species 1023A marked as the second type(e.g. with an asterisk) previously presented to the user 10A. The secondinstance ensures this outcome insofar that the new development option ofthe second-type species 1023B is partly based on navigating away fromthe previously-shown species 1023A and partly based on no otherdeveloped second-type species 1023 being suitable for inclusion withinthe corners of the new map/depiction 297F as detected by the responsemodule(s) 335.

FIG. 21 illustrates an operational flow 2100 suitable for use with atleast one embodiment, such as may be performed (in some variants) on oneor more servers 1800 or using special-purpose circuitry 1722, 1822. Aswill be recognized by those having ordinary skill in the art, not allevents of information management are illustrated in FIG. 21. Rather, forclarity, only those steps reasonably relevant to describing thedistributed ledger interaction aspects of flow 2100 are shown anddescribed. Those having ordinary skill in the art will also recognizethe present embodiment is merely one exemplary embodiment and thatvariations on the present embodiment may be made without departing fromthe scope of the broader inventive concept set forth in the clauses andclaims below.

Following a start operation, operation 2110 describes obtaining one ormore user profiles (e.g. one or more interface modules 331 obtaining oneor more user profiles). This can occur, for example, in a context inwhich such descriptions are provided in an account creation protocol.See FIG. 25.

Operation 2120 describes obtaining a map region of interest as aselection, default, zoom, or navigation (e.g. one or more responsemodules 336 obtaining a map region of interest as a selection, default,zoom, or navigation).

Operation 2130 describes obtaining a marker density between a lowerboundary related to local species inventory and an upper boundaryrelated to display size (e.g. one or more indexing modules 335 obtainingan ideal number or range of each type 370 of project that should beshown given how many suitable within-map species are available, whattypes are most popular among a cohort of users including the currentuser, what the priorities 833 are of the available options that areready to show within the user-designated geographic range, and othersuch determinants). This can occur, for example, in a context in which aneural network makes such determinations over time using a scoringprotocol 476D partly based on a user continuing the session or partlybased on such users initiating an offer during the session.

Operation 2140 describes extending beyond a favored range of position orproject size for one or more favored project types (e.g. a first controlmodule 332 determining that one or more ranges 1242 of position 1212 orproject size 1211 are inadequately circumscribed for a most-favoredproject type 370). This can occur, for example, in a context in which auser has made large recent strides to indicate a preference for seeinglarger projects further to the west than have been shown and in whichthere is no convenient way for a user to inform the system that progressalong these lines is taking too long.

Operation 2150 describes extending project type diversity within one ormore favored ranges (e.g. a second control module 332 restoring orexpanding a diversity of types that was earlier presented to the user inthe same session within one or more favored ranges 1242 by triggeringdevelopment 900, 1400 of an inchoate species 1027 or by sifting othersuitable sites that may prove attractive within the one or more ranges1242). This can occur, for example, in a context in which a singleunfavored species of each type 370 suffices to demonstrate the user'sdisinterest in those types because those single unfavored species 1024were each plottable amongst several favored species 1025 and in whichsuch sufficiency could not otherwise be established at a desired levelof magnification without markers 785 making other markers 785 illegible.

FIG. 22 illustrates an operational flow 2200 suitable for use with atleast one embodiment, such as may be performed (in some variants) on oneor more servers 1800 or using special-purpose circuitry 1722, 1822. Aswill be recognized by those having ordinary skill in the art, not allevents of information management are illustrated in FIG. 22. Rather, forclarity, only those steps reasonably relevant to describing thedistributed ledger interaction aspects of flow 2200 are shown anddescribed. Those having ordinary skill in the art will also recognizethe present embodiment is merely one exemplary embodiment and thatvariations on the present embodiment may be made without departing fromthe scope of the broader inventive concept set forth in the clauses andclaims below.

Operation 2215 describes obtaining an identification of first and secondcomposite project sites both containing a reference parcel, wherein thefirst assemblage includes a first parcel adjacent the reference parcelin combination with the reference parcel, wherein the second assemblageincludes a second parcel adjacent the reference parcel in combinationwith the reference parcel, and wherein a reference recordation signalsthat the reference parcel is not commonly owned with the first or secondparcels (e.g. one or more interface modules 331 receiving or generatingan identification of component parcels in respective first and secondproject sites 121, 122 both containing a reference parcel 160, whereinthe first project site 121 includes a first parcel 161 adjacent thereference parcel 160 in combination with the reference parcel 160,wherein the second project site 122 includes a second parcel 162adjacent the reference parcel 160 in combination with the referenceparcel 160, and wherein one or more public records 314 signal that thereference parcel 160 is not commonly owned with the first parcel 161 orwith the second parcel 162). This can occur, for example, in a contextin which each interface module 331 manifests parcel identifiers 448A-B,boundary coordinates 861, and other such information about the parcelsas respective (instances of) voltage configurations 351 thereof.

Operation 2230 describes recursively or otherwise obtaining first andsecond building models of the first assemblage each based on arespective application of first and second deterministically repeatablespeciation protocols to a first multi-parcel-site-specific seedingconfiguration associated with the first assemblage (e.g. an instance ofa speciation module 333 obtaining first and second deterministicallyidentified instances of species 201 including one or more simulatedbuilding models 202 depicted upon the first project site 121 whereineach such species 201 is based on a respective application 1577 of atleast first and second speciation protocols 476B to a firstmulti-parcel-site-specific seeding configuration associated with thefirst project site 121). This can occur, for example, in a context inwhich the first speciation protocol 476B comprises a single-shelteralgorithm like that of Table 3 herein; in which the second (instance ofa) speciation protocol 476B comprises a multi-building model algorithmlike that of Table 4 herein; and in which seeding 475 for suchalgorithms comprises a street address or other parcel identifier 448A,coordinates 861, or other repeatable designation of the reference parcel160 together with a repeatable designation of other parcels of the firstproject site 121 as respective voltage configurations 353.

Operation 2240 describes recursively obtaining first and second buildingmodels of the second assemblage each based on a respective applicationof first and second deterministically repeatable speciation protocols toa first multi-parcel-site-specific seeding configuration associated withthe second project site (e.g. a second instance of a speciation module333 obtaining first and second deterministic instances of species 201including one or more simulated building models 202 depicted at leastpartly upon the second project site 122, wherein each such species 201is based on an application 1577 of respective speciation protocols 476Bto a multi-parcel-site-specific seeding 475 associated with the secondproject site 122). This can occur, for example, in a context in whichthe “first” speciation protocol 476B is a multi-building model algorithmlike that of Table 4 herein; in which the “second” speciation protocol476B is a single-shelter algorithm like that of Table 3 herein; and inwhich seeding 475 for such algorithms comprises a reference parcelidentifier 448A or other repeatable designation of the reference parcel160 together with a repeatable designation of other parcels of thesecond project site 122 as respective voltage configurations 353.

Operation 2250 describes causing the first building model of the firstassemblage to be prioritized over the second building model of the firstassemblage and to be presented to a user of a visual display in lieu ofthe second building model based on a machine-learning-based scoringprotocol (e.g. a first instance of an authorization module 334 causing afirst species 201 of the first project site 121 to be ranked above asecond instance of an alternative species of the first project site 121and to be presented to device user 10C using one or more display screens1712 in lieu of the alternative species based on amachine-learning-based score 481, rank 488, or other evaluation). Thiscan occur, for example, in a context in which such evaluation data 480comprises explicit preferences 866 from the device user 10C; apreference model 202 derived from search, presentation duration, orother user action history 863; or no preference data at all.Alternatively or additionally, such preference data relating to one ormore entities 610 may be obtained or used (or both) as a primary aspectof a default prioritization 833, 1933 or supervised-learning-typeprotocol 476.

Terms like “supervised-learning-type” refer herein not only tosupervised learning per se but also to other technologies in which inputdata is mapped to output data based on training data that pairs numerousvector-valued input objects (e.g. defining composite project sites,speciations, or other such operational data 405) each to a correspondingpreferable output value 853 (e.g. a valuation 380, score 481, latitude,offset, selection, rank 488, authorization, size estimate, or otherpreference indication 840) using one or more user-provided inductivebiases (e.g. observed user actions 1594). In light of teachings herein,for example, such machine learning implementations can be gleaned fromsearch terms 1590 or other user inputs 1708 from such entities 610without any undue experimentation.

Operation 2265 describes causing the first building model of the secondassemblage to be prioritized over the second building model of thesecond assemblage, to displace the first building model of the firstassemblage, and to be presented via the visual display in lieu of thesecond building model of the second assemblage all partly based on themachine-learning-based scoring protocol and partly based on one or morepreference-indicative actions of the user of the visual display (e.g. asecond instance of an authorization module 334 and one or more indexingmodules 335 jointly causing the first species 201 of the second projectsite 122 to be deemed preferable over the second species of the secondproject site 122; to replace or partly occlude a rendering of the firstspecies 201 of the first project site 121; and to be presented to theuser in lieu of the second species of the second project site 122 partlybased on the machine-learning-based scoring protocol 476D and partlybased on one or more preference-indicative actions 1594 of the user).This can occur, for example, in a context in which an authorizationmodule 334 manifests an identifier of the first project site as avoltage configuration 354 thereof; in which the rendering of the firstspecies 201 of the first project site 121 is thereby initially presentedto the user; in which an indexing module 335 manifests a touchscreenactivation or other preference-indicative user action 1594 as a voltageconfiguration 355 to index to a next-most-preferable option; in whichthe visual display presents (the first species 201 of) the secondproject site 122 in response 1525; and in which multiple visual displaydevices would otherwise be required to allow the automatically createdmessage draft to be tailored by the user before transmission.

Operation 2280 describes causing a draft offer-descriptive messagecontaining a parcel identifier, a parcel valuation, and a premiumvaluation to be presented simultaneously via the visual display as areal-time response to a request associated with the reference parcelfollowing a presentation of one or more such building models via thevisual display (e.g. one or more control modules 332 causing a draftoffer-descriptive message containing a street address or other parcelidentifier 448A; a public-records or independent-party-provided parcelvaluation 380; and premium valuation 10-50% higher than the prior parcelvaluation 380 to be presented simultaneously via the one or more displayscreens 1712 as a real-time response 1525 to a request 851 associatedwith the reference parcel 160 following a presentation 834 of one ormore such building models 202 corresponding to the message). This canoccur, for example, in a context in which the control module(s) 332manifest the draft message in a memory (e.g. as a voltage configuration357 on electrical nodes 347 thereof) and in which parcel adjacency wouldnot otherwise get appropriately proactive consideration when undertakingto acquire real property parcels from multiple respective sellers.

Operation 2290 describes causing numerous additional pairings of asubject parcel identifier with a corresponding valuation to be presentedtogether after a corresponding building model all within a one-hourperiod (e.g. one or more response modules 336 serially or otherwisecausing (at least) dozens of additional pairings of a street address ofa subject parcel 160 each with a corresponding published or otherconventional valuation 380 of that parcel to be presented together aftera corresponding species 201 of a preferable project site 121 of thatparcel all within a one-hour period). This can occur, for example, in acontext in which the user has reviewed project sites 121-122 andcorresponding species 201 as a semi-automatic protocol for validatingparcel suitability 519; in which the response module(s) 336 manifestsuch pairings in a proposed offer batch of more than half of the parcelsin those validated project sites; in which the user has reviewed a draft(version of a) message for at least one such parcel on a prior occasion;in which such validations are manifested as a voltage configuration 356on electrical nodes 346 thereof); in which a transmission module 338 maythereafter send such offer-descriptive content 890 to email or otheraddresses 453 associated with each owner name 451 thereof; and in whichmore than 12 contemporaneous parallel offers all within the one-hourperiod and all based on the same machine-learning-based scoring protocolwould otherwise remain unattainable. Alternatively or additionally, the“corresponding” valuations may include premium valuations each at leastpartly based on a conventional valuation 380 of the subject parcel(derived as a markup percentage designated by the user, e.g.).

In light of teachings herein, numerous existing techniques may beapplied for configuring special-purpose circuitry or other structureseffective for obtaining real property data and presenting key aspects ofpotential developments thereon as described herein without undueexperimentation. See, e.g., U.S. patent Ser. No. 10/679,205 (“Systemsand methods regarding point-of-recognition optimization of onsite userpurchases at a physical location”); U.S. patent Ser. No. 10/528,652(“Generating predictive models for authoring short messages”); U.S.patent Ser. No. 10/521,943 (“Lot planning”); U.S. patent Ser. No.10/521,865 (“Structural characteristic extraction and insurance quotegeneration using 3D images”); U.S. patent Ser. No. 10/510,087 (“Methodand apparatus for conducting an information brokering service”); U.S.patent Ser. No. 10/459,981 (“Computerized system and method forautomatically generating and providing interactive query suggestionswithin an electronic mail system”); U.S. patent Ser. No. 10/496,927(“Systems for time-series predictive data analytics, and related methodsand apparatus”); U.S. patent Ser. No. 10/467,353 (“Building model withcapture of as built features and experiential data”); U.S. patent Ser.No. 10/387,414 (“High performance big data computing system andplatform”); U.S. patent Ser. No. 10/382,383 (“Social media postfacilitation systems and methods”); U.S. patent Ser. No. 10/198,735(“Automatically determining market rental rate index for properties”);U.S. patent Ser. No. 10/192,275 (“Automated real estate valuationsystem”); U.S. patent Ser. No. 10/190,791 (“Three-dimensional buildingmanagement system visualization”); U.S. Pub. No. 20170109668 (“Model forLinking Between Nonconsecutively Performed Steps in a Business Process;and U.S. Pub. No. 20170109638 (“Ensemble-Based Identification ofExecutions of a Business Process”).

Moreover in light of teachings herein, numerous existing techniques maybe applied for implementing extraction, modeling, scoring, selection,feature augmentation, speciation, rendering, and other developmentprotocols as described herein without undue experimentation. See, e.g.,U.S. patent Ser. No. 11/158,118 (“Language model, method and apparatusfor interpreting zoning legal text”); U.S. patent Ser. No. 11/157,930(“Systems and methods for defining candidate and target locations basedon items and user attributes”); U.S. patent Ser. No. 11/134,359(“Systems and methods for calibrated location prediction”); U.S. patentSer. No. 11/068,385 (“Behavior driven development test framework forapplication programming interfaces and webservices”); U.S. patent Ser.No. 11/044,393 (“System for curation and display of location-dependentaugmented reality content in an augmented estate system”); U.S. patentSer. No. 10/992,836 (“Augmented property system of curated augmentedreality media elements”); U.S. patent Ser. No. 10/983,026 (“Methods ofupdating data in a virtual model of a structure”); U.S. patent Ser. No.10/860,023 (“Systems and methods for safe decision making of autonomousvehicles”); U.S. patent Ser. No. 10/818,082 (“Method and system forparametrically creating an optimal three dimensional buildingstructure”); U.S. patent Ser. No. 10/706,057 (“Presenting groups ofcontent item selected for a social networking system user based oncontent item characteristics”); and U.S. patent Ser. No. 10/296,961(“Hybrid recommendation system”).

FIG. 23 illustrates an operational flow 2300 suitable for use with atleast one embodiment, such as may be performed (in some variants) on oneor more servers 1800 using special-purpose circuitry 1822. Operation2315 describes obtaining an identification of first and secondassemblages both containing a reference parcel, wherein the firstassemblage includes a first parcel adjacent the reference parcel incombination with the reference parcel, wherein the second assemblageincludes a second parcel adjacent the reference parcel in combinationwith the reference parcel, and wherein a reference recordation signalsthat the reference parcel is not commonly owned with the other parcels(e.g. one or more interface modules 331 generally configured and invokedas described above).

Operation 2335 describes obtaining multiple building models of each ofthe assemblages each based on a respective application of multipledeterministically repeatable speciation protocols to a respectivemulti-parcel-site-specific seeding configuration (e.g. one or morespeciation modules 333 generally configured and invoked as describedabove).

Operation 2350 describes causing a first building model of a firstassemblage thereof to be prioritized over a second building model of thefirst assemblage and to be presented via a visual display based on agiven scoring protocol (e.g. one or more authorization module 334generally configured and invoked as described above). This can occur,for example, in a context in which such diverse, selective,consensus-driven, or other controllable presentations 834 allow adeveloper to see and act upon vetted search results that could not havebeen visualized via a single display screen 1512 and in which suchcomplex arrangements of property transfers would otherwise be toodiffuse to allow any such multi-parcel-site-specific development tooccur without government compulsion or significant duress.

FIG. 24 illustrates an operational flow 2400 suitable for use with atleast one embodiment, such as may be performed (in some variants) on oneor more servers 1800 using special-purpose circuitry 1822. Operation2465 describes causing a first building model of a given assemblage tobe prioritized over a second building model of the assemblage, todisplace a third building model of another assemblage, and to bepresented via a visual display all as a conditional response 1525 partlybased on a machine-learning-based scoring protocol and partly based onone or more preference-indicative actions 1594 signaling a referenceparcel of the composite project sites (e.g. one or more authorizationmodule 334 generally configured and invoked as described above via thevisual display).

Operation 2480 describes causing a subsequent presentation of a parcelidentifier simultaneously with a corresponding premium valuation atleast partly based on a prior valuation thereof (e.g. one or moreresponse, configuration, and transmission modules 356-358 generallyconfigured and cooperatively invoked as described above).

FIG. 25 depicts a particular scenario and progressive data flow 2500 inwhich client devices 1700 respectively used by several users 10 or otherentities 610 interact with one or more servers 1800 in regard to apotential transfer of a reference parcel 160 in facilitating developmentof a multi-parcel site. A species development server 1800A receivesparcel descriptions 2515 and other reference data 2520 such as site maps835, zone types 370, valuations 380, or other operational data 405.Thousands of diverse stock species 2540A are developed in numerous urbanand suburban regions across multiple price ranges and zoning-compatibleland uses. Many such species posit a largest superset project site 123(nominally) incompatible with an inclusion of additional adjacent land.Many others are based upon price-appropriate subset thereof that areconsistent with interior area sizes or other aspects of a particularinvestor's preference profile 867. Meanwhile several entities 610undergo registration, which may provide overt expressions 850 ofpreference 866 to interface server 1800B that may inform a mostpromising inventory 2550A. For example property search developmentcriteria 2542 may provide operating parameters 831 to development serverto enhance the available inventory 2550A through further speciation, aswell as to support an immediate service 2555 of rendering candidateproject sites and building model configurations 1572.

After an implementation delay 2549 of several additional hours or days,such focused search parameters 2545 may have been used by thedevelopment server(s) 1800A to develop additional project sites (andspecies thereof) of likely interest (as manifested by a compatibility484, rank 488, or other score 481 thereof) in an enhanced inventory2550B. Moreover prior offers or other available owner data 450,contractual restriction status may be useful to update 2560 tofacilitate real-time parcel selection refinement 2570, offercustomization 2585, and the resulting firm offers or otheroffer-descriptive content 890 being distributed to owners of parcels ofconfirmed interest.

FIG. 26 depicts a particular scenario and progressive data flow 2600 inwhich client devices 1700 respectively used by several device users10A-D (e.g. as owner entities or developers) interact with one or moreservers 1800 pursuant to various protocols 476 that facilitate featureaugmentation such as efficient machine-assisted development ofmulti-parcel sites 121-123. As shown some existing users in a cohorteach send one or more selections 2694A that signal one or morecomponents 769 of types of virtual construction species 1021-1026 ofinterest, negatively or positively. Such selections 2694 (e.g. fornavigation, inspection, or requested processing) may, for example,indirectly indicate which sites, sizes, or other such components 769pertain to alternative offerings worth developing. User 10C likewisesends selections and other indications 840A to one or more interfaceservers 1800B so as to allow an interactive presentation of anestablished inventory of virtual depictions 297 and otherproject-descriptive content 890 to each visiting user 10. Suchselections 2694 and other indications 840B are sampled or otherwisesummarized and forwarded as an occasional distillation 2698A to backendserver 1800A. Server 1800A uses such indications 840 to guidedevelopment 1400A pertaining to various processing operations such asspeciation 975 and rendering 976, after which a resulting set ofexpressions 850 (e.g. lists 473 or maps 835) is sent to server 1800B.

After at least one overnight delay 2649, one or more selections 2694 orother indications 840C are received from a new device user 10D, such asfor establishing an account and an initial geography of interest. Inresponse to this and other indications 840D of user preference 466, 866one or more modules 331-338 within or accessible to server 1800Bmanifest a succession of preliminary depictions 297F featuring one ormore regional maps 835 populated with a tailored assortment of virtualspecies 201, 1021-1026 shown among existing real-world structures.Additional iterations 2674A of navigational indications 840E (e.g. paths1087 or control activations) trigger corresponding refined versions 862of inclusions 788, prioritizations 833, messages 1560, or other updateddepictions 297 as described herein. Such user actions also form a basisfor one or more incremental preference distillations 2698B that areprovided to backend server 1800A. This allows efficient ongoingimprovement of existing project sites or development 1400B of newmulti-parcel sites 121-123 or other map inclusions 788. See FIGS. 1-12.

In some variants, for example, such development 1400B may be informed byone or more common terms 1590 or other newfound associations 2654between a current user 10D and one or more preference indications 840provided by other users 10. Alternatively or additionally suchdevelopment 1400B may be based on one or more new distillations 2698resulting from a newly-implemented learning/extraction protocol 476G.Such distillation 2698C may occur in response to selections 2694B orother indications 840 (e.g. using buttons or other controls 225 asdescribed herein) in reference to each presented depiction 297G or otherexpression 850 through numerous iterations 2674B. Such iterations 2674Bmay guide efficient ongoing development 1400 as well as betterunderstood associations 2654 among users, such as an automaticassignment of a now-active user 10D to a cohort of other device userswho have apparent similarities and whose prior actions are accordinglyuseful for predicting one or more preferences of the now-active user 10Das a passive inference, whereby computer resource waste is reduced asfurther described herein.

In some variants vivid new spatial depictions 297 of best-in-classspecies 1021-1025 tailored to be of particular interest to a particularuser 10D are generated. This can occur, for example, in a context inwhich one or more indexing modules 335 (e.g. in a client device 1700 orserver 1800) distill menu selections 2694A and other indications 840A-Bof user preference 466, 866 into aggregated profiles 867 that typifyrespective cohorts of users (e.g. commercial or residential developers)each characterized by preferences not shared by some other factions ofusers 10.

In some variants a single type 370, 670 of users has triggered anassociation among them by having indicated a similar land useclassification 838, geographic dimension 368, or other such projectattribute 617 (or combinations thereof) so that a learning module 337uses an action history 863 of some users 10A-C in a cohort as trainingdata 405-407 to predict one or more preferences 466, 866 of another user10D who is later assigned to the cohort. In some contexts, for example,such preferences 466, 866 selectively qualify some composite projectspecies 1021-1025 for prioritization (e.g. over one or more otherspecies 1026) and a pre-existing inventory 2550 of options 1984A-Dincludes one or more species 1021-1022 were initially deemed worthy ofinclusion 788 (e.g. in prioritization 1933A) but are later omitted orotherwise demoted on behalf a particular user 10D (e.g. inprioritization 1933B). Moreover such incremental prioritizations can beongoing and may influence how limited computing resources may be appliedfor development 1400 and expression 850 in the near term (e.g.overnight) or even in real time (e.g. within less than 30 seconds afterone or more indications 840B-C to which they are responsive.

After a delay 2649 of days or weeks, an indication 840C of what projecttypes, locations, or other attributes 617 are impliedly of interest. Inresponse, from a developed inventory of local species a map version 862Aor image 1096 thereof, model rendering, or other detailed depiction 297For requested or suitable species 1021-1025 is presented via a localdevice 1700 to user 10D. And additional indications 840D-E (e.g.favorable signals 703A or other user actions 1594 pertaining to anavigation, magnification, closer inspection, or other preference 866)from respective users 10 signal which candidate species 1021-1023 arefittest for prioritization in a next (instance of a) version 862 to eachsuch user 10 as well as one or more other users within a cohort.

At programmatic intervals a distillation 2698B of user indicativeinformation causes additional development 1400B (e.g. featuring newextraction 973, speciation 975, rendering 976, or a combination thereof)to occur as a tailored response 1525 to a detected status 1522 (e.g. asufficient number of iterations 840C-E and or other counted eventsexceeding a threshold as detected by an indexing module 335). This maycause an interface module 331 to express a second project siteprioritization 833 1933B later favoring the third-type project option1984C (e.g. corresponding to a second-type species 1022 or third-typespecies 1023 over the first-type and second-type project options 1984A,1984B to the first device user 10D.

In some contexts, for example, such a changed prioritization 1933B maybe partly based on an explicit indication 840E of a preference 866 ofuser 10D for the third-type composite project option 1984C over thesecond-type project option 1984B (e.g. directly from user 10D havingdisparaged the second-type composite project option 1984B) and partlybased on an implicit indication 840 of an apparent preference 866 ofuser 10D for the third-type composite project option 1984C over thefirst-type project option 1984A inferred from other users 10A-B who have(dismissed or otherwise) disparaged the first-type composite projectoption 1984A) being related to user 10D.

Alternatively or additionally such a status may trigger an additionalexpression 850 (e.g. comprising another instance of a now-favoredspecies 1023) as a conditional response 1525 (at least partly) based onthe implicit and explicit indications 840 of the preference 866 of thefirst device user 10D for the third-type composite project option 1984C.This can occur, for example, in a context in which an association 2654between user 10D and other users 10A-C is

After an implementation delay 2649 of several additional hours or days,such focused search parameters 2645 may have been used by thedevelopment server(s) 1800A to develop additional project sites (andspecies thereof) of likely interest (as manifested by a compatibility484, rank 488, or other score 481 thereof) in an enhanced inventory2650.

FIG. 27 illustrates an operational flow 2700 suitable for use with atleast one embodiment, such as may be performed (in some variants) on oneor more servers 1800 or using special-purpose circuitry 1722, 1822. Aswill be recognized by those having ordinary skill in the art, not allevents of information management are illustrated in FIG. 27. Rather, forclarity, only those steps reasonably relevant to describing thedistributed ledger interaction aspects of flow 2700 are shown anddescribed. Those having ordinary skill in the art will also recognizethe present embodiment is merely one exemplary embodiment and thatvariations on the present embodiment may be made without departing fromthe scope of the broader inventive concept set forth in the clauses andclaims below.

After a start operation, 2705, operation 2710 describes obtaining a useraction relating to a reference parcel. Control then passes to operation2715.

Operation 2715 describes beginning a (potentially iterative) loopbeginning at operation 2720 in relation to one or more compatibleoptions (e.g. respective sets of one or more parcels that can becombined with the reference parcel 160 into an assemblage or othercomposite site using one or more restrictions 517 provided by orotherwise suitable for a current user 10 or cohort).

Operation 2720 describes evaluating a current parcel add option (e.g.using one or more scoring protocols provided by or otherwise suitablefor a current user 10 or cohort). Control then passes to operation 2725.

Operation 2725 describes determining whether an added parcel is highlysuitable (e.g. using one or more scoring protocols 476D that ascertainwhether a quantified evaluation exceeds a threshold). If so then controlpasses to operation 2735, otherwise to operation 2775.

Operation 2735 describes deeming the parcel to be of primary interest.Control then passes to operation 2740.

Operation 2740 describes associating a current primary parcel with oneor more entities (e.g. using one or more lookup protocols 476 thatascertain who owns or manages a parcel that might be acquired). Controlthen passes to operation 2745.

Operation 2745 describes beginning a (potentially iterative) loopbeginning at operation 2755 in relation to one or more entities who ownor manage a primary parcel. Control then passes to operation 2755.

Operation 2755 describes determining whether one or more alternateparcels owned or managed by the same entity are identified (e.g. usingone or more lookup protocols 476 that handle ownership or listingrecords 414). If so then control passes to operation 2760, otherwise tooperation 2765.

Operation 2760 describes developing one or more alternate parcels ownedor managed by the same entity (e.g. using one or more scoring anddevelopment protocols 476D, 476H). Control then passes to operation2765.

Operation 2765 describes iterating a loop begun at operation 2745 ifappropriate. Otherwise control then passes to operation 2775.

Operation 2775 describes iterating a loop begun at operation 2715 ifappropriate. Otherwise control then passes to operation 2780.

Operation 2780 describes automatically generating a draft notificationpertaining to at least one newly-developed alternate parcel (e.g. usingone or more notification protocols 476). Control then concludes flow2700 at operation 2799.

The following table illustrates a root genetic algorithm suitable foruse (e.g. by one or more development servers 1800A) in one or morevariants described herein:

TABLE 1 Algorithm 1 root genetic algorithm  1: generation₀ = for Mspecies:  2:  for N specimens:  3:   generate random specimen (accordingto strategy)  4: generation_(n+1) = map for each species ingeneration_(n):  5:  map for each specimen in species:  6:   if (randomprobability 70%):  7:    // do crossover  8:    if (random probability1%):  9:     get random other specimen from random other species 10:   else: 11:     get random other specimen from this species 12:   specimen = crossover(specimen, other specimen) 13:   if (randomprobability 25%): 14:    specimen = mutate(specimen) 15:   if (randomprobability 0.5%): 16:    specimen = smooth(specimen) 17:   specimen =normalize(specimen) 18:   specimen.fitness = evaluate(specimen) 19:  return specimen 20: fitness change rate = 1000 21: best specimen ever= specimen having max(fitness from specimens in generation₀) 22: recursethrough generations: 23:  // keep track of the best specimen we've everseen 24:  best specimen = specimen having max(fitness from specimens in generation) 25:  if best specimen.fitness > best specimen ever.fitness:26:   best specimen ever = best specimen 27:  // moving average to seehow quickly we're improving 28:  fitness delta = best specimen.fitness −best specimen ever.fitness 29:  fitness change rate = (92% * fitnesschange rate) + (8% * fitness  delta) 30:  if (fitness change rate < minchange rate or generation number >=  1800): 31:   abort 32: return bestspecimen ever

The following table illustrates an arena sizing algorithm suitable foruse (e.g. by one or more development servers 1000C) in some variantsdescribed herein:

TABLE 2 Algorithm 2 arena sizing algorithm  1: // The arena is the localgrid of cells in which specimens can be generated. A parcel's lot areais mapped onto a grid of fixed-size cells with the following algorithm: 2: parcel street line = the street line with the same name as theaddress of the parcel  3: parcel orientation = angle from parcelcentroid to parcel street line  4: // This is used to transform pointsfrom global coordinates to an approximately  5: accurate localcoordinate system for the parcel based on the parcel centroid  6:tolocal(centroid {x, y}, point {x, y}) =  7:  radius of earth =6,371,009 m  8:  centroid radians = centroid * (π/180)  9:  pointradians = point * (π/180) 10:  // cos(centroid radians.y) accounts forconvergence of longitude lines toward the poles 11:  return { 12:   x =radius of earth * (point radians.x − centroid radians.x) * cos(centroidradians.y) 13:   y = radius of earth * (point radians.y − centroidradians.y) 14:  } 15: // Project the polygon to our local coordinatesystem in metres 16: local poly = map tolocal(parcel centroid, point)for each point in parcel polygon // lot lines 17: // Rotate the parcelto its orientation 18: local poly = rotate(local poly, parcelorientation) 19: 20: // Place the grid around the center of the rotatedpolygon. This rectangle will be 21: the defining shape of our arena 22:bounding box = make bounding box for local poly: { 23: MinX = min(x)from local poly 24: MinY = min(y) from local poly 25: MaxX = max(x) fromlocal poly 26: MaxY = max(y) from local poly 27: } 28: // Size each cellinside the bounding rectangle - these are the discrete units that can befilled with different data 29: // ln = natural logarithm 30: smallerside = smaller of (MaxX − MinX) or (MaxY − MinY) from bounding box 31:cell size = ln(1 + smaller side / 10) / 1.5 32: 33: // Create a mask ofcells within the bounding box that aren't actually within the parcelpolygon 34: for each discrete cell {x, y} from {0, 0} in bounding box /cell size: 35:  cell point = minimum point from bounding box + (cell *cell size) 36:  cell rectangle = {cell point, cell point + cell size}37:  if (cell rectangle is fully inside local poly): 38:   mask[x, y] =true 39:  else: 40:   mask[x, y] = false 41: // Defined for convenience:42: arena width = floor(bounding box.(MaxX − MinX) / cell size) 43:arena height = floor(bounding box.(MaxY − MinY) / cell size) 44: 45: //The arena is defined as: 46: arena = { 47:  world poly, // the originalpolygon in world coordinates 48:  parcel orientation, 49:  centroid, 50: local poly, 51:  bounding box, 52:  cell size, 53:  arena width, 54: arena height, 55:  mask 56: } 57: // All array shapes and coordinateswithin strategies will typically be in arena cells.

The following table illustrates a single-shelter algorithm suitable foruse in some speciation described herein:

TABLE 3 Algorithm 3 single-shelter algorithm  1: // data structures  2:params := {  3:  // We define multiple levels for a given configurationat which structures are built up  4:  levels = array [N] {  5: height, 6: // Areas where building is not permitted measured from the lot line 7: setbacks { left, right, back, front }  8: },  9: // Parkingparameters  10: assumed unit size for parking,  11: parking per dwellingunit,  12: surface parking space area,  13: underground parking spacearea,  14: underground parking permitted = boolean,  15: }  16: specimen:= {  17: params,  18: // A particular cell can be in one of thefollowing states:  19:  shape array [X] [Y] arena cells:  20:   valuesfor:  21:    parking,  22:    empty,  23:    level₁,  24: level₂,  25:...,  26: level_(n)  27: }  28: // genetic algorithm operations  29:random specimen =  30:  for each cell {x, y}:  31:   if mask[cell] =true:  32:  shape [cell] = random either empty or level₁  33: else:  34: shape [cell] = empty  35: normalize(specimen) =  36:  set all cellswhere mask is false to empty  37: crossover(specimen1, specimen2) =  38: axis = randomly choose either X or Y  39:  split = randomly choose anycoordinate within arena on axis  40:  return new specimen {  41:   shape= cells from specimen 1 where coordinate on axis < split and  42:   cells from specimen2 where coordinate on axis >= split  43:  }  44:// Smooth out the specimen, somewhat averaging each cell across itsneighbours  45: smooth(specimen) =  46:  map for each cell {x, y} inspecimen:  47:   // Whichever distinct value has the highest sum score 48:   // For example, if there are three instances of level₂ in theneighbours  49: // and others are empty, level₂ has a score of 6 andempty has a score of 3, so  50: // we would choose level₂  51:   newvalue = value with highest score where:  52:    score 1 for value at {x,y}  53:    // neighbors  54:    score 2 for value at {x − 1, y − 1}  55:   score 2 for value at {x + 1, y − 1}  56:    score 2 for value at {x −1, y + 1}  57:    score 2 for value at {x + 1, y + 1}  58:mutate(specimen) =  59:  origin = randomly choose point wheremask[point] = true  60:  level = n if shape [point] is level_(n)otherwise 0  61:  // either raise or lower structure  62:  new level =(if (random) level + 1 else level − 1) clip between 0 and max level  63: // Expand the largest contiguous rectangle that fits fully within theenabled part of  64:  // the mask  65:  states = [  66:   X minus,  67:  X plus,  68:   Y minus,  69:   Y plus  70:  ]  71:  done = [false,false, false, false]  72:  rectangle = [origin, origin]  73:  loopthrough states as state:  74:   if not done [state]:  75:   // Ensure wecan continue to expand  76:   if all points in expanded rectangle havemask[point] = true:  77:   expand rectangle in the direction of state 78:  else:  79:   // We can't expand more in this direction  80:  done[state] = true  81: else:  82:  if all elements of done are true: 83:   break loop  84: randomly choose an action from:  85:  Set:  86:  set all cells within rectangle to new level // where 0 means empty 87:  Set Parking:  88:   set all cells within rectangle to parking  89: Shear:  90:   set all cells outside of rectangle to empty  91:  Flip X: 92:   mirror cells within rectangle along X axis  93:  Flip Y:  94:  mirror cells within rectangle along Y axis  95: fitness(specimen) = 96:  // Compute all of the input parameters  97:  withlengths(specimen, level) for each level  98:  with lengths(specimen,parking)  99:  with heights(specimen, lengths) 100:  total storeys =max(heights) 101:  for each level: 102:   storeys = heights [level] 103:  limits = conditional limits for(storeys, total storeys) 104:   withlength suboptimality(specimen, limits, lengths [level]) 105:   withsetback interference(specimen, limits, lengths [level]) 106:   withcoverage(specimen, limits, level) 107:  with lengthsuboptimality(specimen, {min gap = 40 ft, min run = 18 ft}, lengths[parking]) 108:  with area stats(specimen, heights) 109:  with parkingstats(specimen, area stats) 110: with connectedness(specimen) 111: withcount walls(specimen, axis) for axes X and Y 112: return sum: 113:  40 * (5 + (area stats.spread / total # cells in arena)) + 114:   (ifparams.max FAR is set: 115:    if area stats.FAR < params.max FAR: 116:    −1 * (1 + 110 * (max FAR − area stats.FAR))³ 117:    else if areastats.FAR > params.max FAR: 118:     // being over the max FAR is worse119:     −10 * (1 + 110 * (area stats.FAR − max FAR))³ 120:    else:121:     0 122:   else: 123:    2 * (area stats.area)^(1.3) 124:   ) +125:   1 * (1 + count walls.cells per wall)² + // long walls 126:  −10 * (count walls.walls)³ + // many walls 127:   2 *connectedness.connected + 128:   −2000 * (connectedness.alone)² + 129:  1 * (area stats.centrality)² + 130:   −5 * (1 + sum ( 131:    (10 *under gap)² + 132:    (10 * under run)² + 133:    (0.3 * one side)² +134:    (10 * inner hole)² + 135:    (10 * good points)² + 136:   (good)^(0.6) + 137:    if this is the first level and there is morethan one level: 138:     (20 * gap)³ 139:    else 0 140:   ) for eachlength suboptimality) + 141:   −5000 * (sum of setback interference foreach level and side)² + 142:   −5 * (sum of coverage.excess for eachlevel)² + 143:   −10000 * (only if greater than 0: 144: (parkingstats.underground parking + parking stats, surf ace parking) − 145:parking stats.required parking) + 146: (if parking stats.under groundparking area > 0: 147:  // disincentivize underground parking vs.surface parking 148:  −1000 * (1 + parking stats.underground parkingarea / lot area)^(1.2)) 149: // Measure lengths of either under oron/over level accessible from each point 150: // Result is an array foreach axis where each cell maps to a value of either: 151: // - Outside:cell is masked, outside the parcel 152: // - Gap(length): this cell ispart of a gap of “length” cells below the level 153: // -OutsideGap(length): same as above but gap is on the outside edge of theparcel 154: // - Run(length): this cell is part of a run of “length”cells on or above the level 155: lengths(specimen, level) = 156:  foreach row for each axis: 157:   for cells in row: 158:    if mask[cell] =false: 159:     lengths[cell] = Outside 160:   else if shape [cell] >=level: 161:    gather cells on this row while shape[cell] >= level 162:   length = count cells 163:    lengths[cells] = Run(length) 164:  else: 165:    gather cells on this row while shape[cell] < level 166:   length = count cells 167:    if cells are on outside edge of row:168:     lengths[cells] = OutsideGap(length) 169:    else: 170:    lengths[cells] = Gap(length) 171: // Calculate supported heights foreach level based on some concept of stability. 172: // These heightswill be treated as the actual heights for the specimen at each level173: // using the heights in the params as a maximum. 174:heights(specimen, lengths) = 175:  stability = 4.0 176:  last computed =0 177:  max supported = array of numbers for each level 178:  computed =array of numbers for each level 179:  for each level: 180:   averagelength x = calculate mean dimension of 181: all Run(length) in lengthsfor X axis 182:   average length y = calculate mean dimension of 183:     all Run(length) in lengths for Y axis 184:   min mean length = thelowest of either average length x or average length y 185: 186:   // Thetheoretically max supported height at this level. 187:   maxsupported[level] = last computed + (if min mean length > 0 188:      then min mean length * cell size * stability else 0) 189: 190:  min support = min(max supported for all levels up to and includingthis one) 191:   // The actual computed height for this level. 192:  computed[level] = the lowest of either params.levels[level].height or193:      floor(min support) 194:  return [max supported, computed] 195:// “lengths” here is for a particular level or parking, not all of them196: length suboptimality(specimen, limits, lengths) = 197:  gap run x =gap run(limits, lengths, X) 198:  gap run y = gap run(limits, lengths,Y) 199:  one side = 0 200:  inner hole = 0 201:  for each cell {x, y}:202:   if lengths[x, y] for either axis is Run(length): 203:    iflength on either axis > limits.max one side: 204:     one side += 1 205:  if lengths[x, y] for both axes is Gap: // NOT OutsideGap 206:    innerhole += 1 207:  return { 208: sum everything from gap run x and gap runy, 209: one side, 210: inner hole 211: } 212: // “lengths” here is for aparticular level or parking, not all of them 213: // Evaluate statisticsfor the lengths matrix for the axis 214: gap run(limits, lengths, axis)= 215:  min run = limits.min run as cells 216:  min gap = limits.min gapas cells 217:  output = { 218: gap = 0 219: run = 0 220:   under gap = 0221:   under run = 0 222:   good = 0 223:  } 224:  for each row on axis:225:   start = the index of the first OutsideGap in lengths 226:   end =the index of the last OutsideGap in lengths 227:   for each columnbetween start and end: 228:    match lengths[axis, row, column]: 229:    case Gap(length) or OutsideGap(length): 230:      if this is not thefirst or last col and length < min gap: 231:       output.under gap +=(min gap − length) 232:      else: 233:       output, good += 1 234:     if this is not OutsideGap: 235:       out.gap += 1 236:      skipahead by length 237:     case Run(length): 238:      if length < minrun: 239:       output.under run += (min run − length) 240:      else:241:       output.good += 1 242:      output.run += 1 243:      skipahead by length 244:     case Outside: 245:      continue 246:  returnoutput 247: setback interference(specimen, limits, lengths) = 248: interference = {left = 0, right = 0, back = 0, front = 0} 249:  foreach direction {Forward, Reverse} of each axis {X, Y}: 250:  side if(Forward, X) = left 251:  side if (Reverse, X) = right 252:  side if(Forward, Y) = back 253:  side if (Reverse, Y) = front 254:   for eachrow: 255:   initial gap = find first OutsideGap or Gap on axis bydirection in row 256:   if row contains any Run: 257:    setback =limits.setbacks[side] 258:    if initial gap.length < setback: 259:    interference[side] += (1 + setback − initial gap.length)² 260: return interference 261: coverage(specimen, limits, level) = 262:  maxcoverage = limits.max coverage or 100% 263:  count = count cells inspecimen.shape where value is at least level 264:  area in one cell =(cell size)² 265:  coverage = (count * area in one cell) / lot area 266: excess = if coverage > max coverage then coverage − max coverage else 0267:  return {max coverage, coverage, excess} 268: area stats(specimen,heights) = 269:  area in one cell = (cell size)² 270:  area = 0 271: spread = 0 272:  centrality sum = 0 273:  centrality weights = 0 274:for each cell {x,y}: 275:   if specimen.shape[x, y] is level: 276:   area here = area in one cell * (heights.computed[level] in storeys)277:    area += area here 278:    spread += 1 279:    centrality weight= 1 / (1 + (abs(x − width/2) + abs(y − height/2))/2) 280:    centralitysum += area here * centrality weight 281:    centrality weights +=centrality weight 282:  centrality = centrality sum / centrality weights283:  FAR = area / lot area 284:  return {area, FAR, spread, centrality}285: parking stats(specimen, area stats) = 286:  area in one cell =(cell size)² 287:  units for parking = area stats.area / params.assumedunit size for parking 288: required parking = ceil(units for parking *params.parking per dwelling unit) 289: surface parking area = (countcells that are parking) * area in one cell 290: surface parking =floor(surface parking area / params.surface parking space area) 291:underground parking = 292:  if params.underground parking permitted:293: required parking − surface parking or at least 0 294: else: 295:  0296:  underground parking area = 297: underground parking *params.underground parking space area 298: return { 299:  requiredparking, 300:  surface parking, 301:  surface parking area, 302: underground parking, 303:  underground parking area 304: } 305:connectedness(specimen) = 306:  connected = 0 307:  alone = 0 308:  foreach cell {x, y}: 309:   if specimen.shape[x, y] is not empty: 310:   neighbors = values of specimen.shape for following coordinates: 311:    (x − 1, y − 1), 312:     (x − 1, y + 1), 313:     (x + 1, y − 1),314:     (x + 1, y + 1) 315:    connected here = count neighbors wherevalue is same as at x, y 316:    if connected here > 0: 317:    connected += connected here 318:    else: 319:     alone += 1 320:321:  return {connected, alone} 322: count walls(specimen, axis) = 323: walls = 0 324:  wall cells = 0 325:  for each row on axis: 326:   //Edge-detect array − true where the cell is beside a different cell 327:  opposites = array [width] of boolean 328:   for each col on axis: //opposite coordinate of row 329: value here = specimen.shape[axis, row,col] 330: next value = specimen.shape[axis, row, col + 1] or empty 331:opposites[col] = value here is not equal to next value 332: lastopposites = opposites from the previous row or array of all false 333:for each (new edge, old edge) in (opposites, last opposites): 334:  ifold edge = false and new edge = true: 335:   // The value has changed onthis row 336: // while it didn't in the previous row. This is the startof a wall. 337:   walls += 1 338:  if new edge = true: 339:   // Anytime the value changed, this is part of a wall 340:   wall cells += 1341:  return {walls, wall cells}

The following table illustrates a multi-building algorithm suitable foruse in other speciation described herein:

TABLE 4 Algorithm 4 multi-building algorithm  1: building definition :={  2:  width, // x  3:  length, // y  4:  height, // z  5:  floor area, 6:  units, // dwelling units contained within the building  7:  margins{left, right, back, front},  8:  required padding, // additionalunspecified area that must be added around the building  9:  weight //relative weight for choosing this building  10: }  11: params := {  12: max height,  13:  max area ratio, // FAR  14:  max units,  15:  min lotper unit, // minimum lot area per unit  16:  min lot width,  17:  pathsize,  18:  conditional limits,  19:  bake setbacks, // whether to putthe setbacks on the arena rather than on the buildings  20:  measureouter setbacks, // whether to measure setbacks on the lot edge  21:  onelot only, // whether to only generate a single building  22:  accessdirections, // directions from which access is available (street/alleyon that side)  23:  directions, // directions buildings can face  24: no obstructions, // buildings must not be obstructed by other buildingsto access the street  25:  bias to edge, // prefer buildings toward theedge  26:  building definitions  27: }  28: building instance := {  29: building definition,  30:  origin {x, y],  31:  rotation: 0 | 90 | 180| 270 degrees,  32:  padding {left, right, back, front]  33: }  34: pathinstance := {  35:  origin {x, y},  36:  size {w, 1},  37:  rotation: 0| 90 | 180 | 270 degrees  38: }  39: entity := building instance | pathinstance  40: specimen := {  41:  params,  42:  entities array of entity 43: }  44: min change rate = 0.001 // for genetic algorithm  45:rectangle(entity) =  46:  case entity.rotation of:  47:   0 or 180: w =entity.size.w; 1 = entity.size.1  48:   90 or 270: w = entity.size.1; 1= entity.size.w // flipped  49:  {entity.origin, {w, 1}}  50:  51: //This is defined by the parameters of the building definition  52:building instance.size =  53:  {margins.left + width + margins.right,margins.front + length + margins.back]  54: random specimen =  55: insert random pattern(empty specimen)  56: normalize(specimen) =  57: if params.one lot only:  58:   keep only first entity in entities  59: remove entity from entities where entity.direction is not inparams.directions  60:  remove entity from entities whererectangle(entity) intersects arena, mask[false]  61:  if params.measureouter setbacks:  62:   remove entity from entities whererectangle(entity) intersects setbacks  63:  for each entity in entities: 64:   // No overlapping entities.  65:   if rectangle(entity)intersects the rectangle of any prior entity in order:  66:    removeentity  67: crossover(specimen1, specimen2) =  68:  axis = random axisof either X or Y  69:  range = range along axis  70:  split = randompoint within range  71:  new specimen1 = specimen with entities from ( 72:   entities from specimen1 where origin < split along axis +  73:  entities from specimen2 where origin >= split along axis  74:  )  75: new specimen2 = specimen with entities from (  76:   entities fromspecimen2 where origin < split along axis +  77:   entities fromspecimen1 where origin >= split along axis  78:  )  79:  return(normalize(new specimen1), normalize(new specimen2))  80:mutate(specimen) =  81:  // Try again if we fail to insert anything  82: up to 4 times try:  83:  insert random pattern(specimen)  84: ifparams.one lot only:  85:  entity = choose random entity from entities 86:  remove all from entities except entity // keep a random entity 87: // weighted random  88: random definition(params) =  89:  weightssum = sum of weight from params.definitions  90:  for each definition inparams.definitions:  91:   let definition.max number = sum of weightfrom params.definitions prior to and including this definition  92: number = random number between 0 and weights sum  93:  for eachdefinition in params.definitions:  94:   if number < definition.maxnumber:  95:    return definition  96: insert random pattern(specimen) = 97:  rectangle = generate random rectangle within arena  98:  removeentities from specimen where rectangle(entity) intersects rectangle  99: definition = random definition(params) 100:  rotation = randomly choose{0, 90, 180, 270} 101:  movement axis = case rotation of: 102:   0 or180: Y 103:   90 or 270: X 104:  movement direction = case rotation of:105:   0 or 90: Positive 106:   180 or 270: Negative 107:  // each rowis the length of two buildings and a path 108:  for each row alongmovement axis in movement direction within rectangle: 109:   for eachcolumn opposite movement axis 110: in movement direction withinrectangle: 111:  for every 7th column: 112:   insert path instancecovering entire column 113: else: 114:  // buildings are facing the path115:    insert building instance with opposite(rotation) 116:    insertpath instance 117:    insert building instance with rotation 118:fitness(specimen) = 119:  floor area = sum of building definition.floorarea for each building instance 120:  FAR = floor area / lot area 121: units count = sum of building definition.units for each buildinginstance 122:  variations = count distinct building definition for eachbuilding instance 123:  max units = 124:   if params.min lot per unit:125:    density based limit = floor(lot area / params.min lot per unit)126:    lesser of density based limit or params.max units 127:   else:128:    params.max units 129:  excess units = 130:   if max units is setand units count > max units: 131:    units count − max units 132:  else: 133:    0 134:  with edge bias(specimen) 135:  with facingpreference(specimen) 136:  with reachability(specimen) 137: return sum:138:   (if params.max FAR is set: 139:    if FAR < params.max FAR: 140:    −1*(1 + 60* (max FAR − area stats.FAR))² 141:    else if FAR >params.max FAR: 142:     // being over the max FAR is worse 143:    −1 * (1 + 120 * (FAR − max FAR))² 144:    else: 145:     0 146:  else: 147:    2 * (floor area)¹⁴ 148:   ) + 149:   1000 * (unitscount) + 150:   20 * (facing preference)¹·² + 151:   −50 * (4 *(variations − 1))² + 152:   −1 * (100000 * excess units)² + 153:   50 *(4 * reachability.touching paths)² + 154:   −200 * (4 * reachability.notconnected)² + 155:   25 * (4 * reachability.extra path touches)^(1.2) +156:   −100 * (reachability.path edges)² + 157:   (if params.bias toedge: 158:    100 * edge bias 159:   else: 160:    0) 161: facingpreference(specimen) = 162:  sum for each building instance: 163:   caserotation of: 164:    0:  10 * building definition.units // front 165:   90 or 270: 3 * building definition.units // right or left 166:   180: 1 * building definition.units // back 167: reachability(specimen) = 168:  touching paths = 0 169:  not connected = 0 170: extra path touches = 0 171:  path edges = 0 172:  clear to edge = 0173:  is covered by path(x0, y0, x1, y1) = 174:   for each box-wise (x,y) between (x0, y0) and (x1, y1): 175:    if entity intersecting (x, y)is path instance: 176:     return true 177:   otherwise false 178:  //Boxes covering the given edge of the given box to the edge of the arena179:  edge sweep(left, x0, y0, x1, y1) = (0, y0, x0 − 1, y1) 180:  edgesweep(right, x0, y0, x1,y1) = (x1, y0, arena.width, y1) 181:  edgesweep(back, x0, y0, x1, y1) = (x0, 0, x1, y0 − 1) 182:  edgesweep(front, x0, y0, x1, y1) = (x0, y1, x1, arena.height) 183:  // checkedge of box is not obstructed 184:  box clear to(direction, x0, y0, x1,y1) = 185:   if x0 > x1 then swap x0 with x1 186:   if y0 > y1 then swapy0 with y1 187:   (cx0, cy0, cx1, cy1) = edge sweep(direction, x0, y0,x1, y1) 188:   for each box-wise (x, y) between (x0, y0) and (x1, y1):189:    if entity exists intersecting (x, y): 190:     return false 191:  otherwise true 192:  for each building instance entity: 193:   (x, y,w, 1) = rectangle(entity) 194:   units = building definition.units 195:  // each side 196:   path touches = count where true: 197:    iscovered by path(x, y − 1, x + w, y), 198:    is covered by path(x, y +1, x + w, y + 1 + 1), 199:    is covered by path(x − 1, y, x, y + 1),200:    is covered by path(x + w, y, x + w + 1, y + 1) 201:   if pathtouches > 0: 202:    touching paths += units 203:    extra path touches+= (path touches − 1) * units 204:   else: 205:    direction =entity.rotation as direction 206:    accessible = params.accessdirections contains direction 207:    if accessible and box clearto(direction, x, y, x + w, y + 1): 208:     clear to edge += units 209:   else: 210:     not connected += units 211:  for each path instanceentity: 212:   (x, y, w, 1) = rectangle(entity) 213:   path edges +=count where false: 214:    is covered by path(x, y − 1, x + w, y), 215:   is covered by path(x, y + 1, x + w, y + 1 + 1), 216:    is covered bypath(x − 1, y, x, y + 1), 217:    is covered by path(x + w, y, x + w +1, y + 1) 218:  return {touching paths, not connected, extra pathtouches, path edges, clear to edge} 219: edge bias(specimen) = 220: search(left) = (X, forward) 221:  search(right) = (X, reverse) 222: search(back) = (Y, forward) 223:  search(front) = (Y, reverse) 224: average score = none for each side 225:  min score = none for each side226:  max score = none for each side 227:  for each side: 228:   (axis,pattern) = search(side) 229:   gaps = empty list 230:   for each row inarena on axis: 231:    entity = none 232:    counting = false 233:   initial gap = 0 234:    for each column on the opposite axis viapattern: 235:     if there is a building instance entity here ataxis(row, column): 236:      entity = entity here 237:      break loop238:     else: 239:      // Count how much of a gap we have before any240:      // building instances at the beginning of the row ins 241:     // the lot line 242:      if arena.mask[x, y] is true: 243:      counting = true 244:       initial gap += 1 245:      else: 246:      if counting then initial gap += 1 247:    if entity is set andentity is facing side: 248:     push(gaps, cells to metres(initial gap))249:   average score[side] = 1 / (0.1 + (sum gaps / count gaps)/100)250:   min score[side] = 1 / (0.1 + (minimum value in gaps)/100) 251:  max score[side] = 1 / (0.1 + (maximum value in gaps)/100) 252: average score mean = average of average score for each side 253:  minscore mean = average of min score for each side 254:  max score mean =average of max score for each side 255:  return (average score mean +min score mean + max score mean) / 3

While various system, method, article of manufacture, or otherembodiments or aspects have been disclosed above, also, othercombinations of embodiments or aspects will be apparent to those skilledin the art in view of the above disclosure. The various embodiments andaspects disclosed above are for purposes of illustration and are notintended to be limiting, with the true scope and spirit being indicatedin the final claim set that follows.

In the numbered clauses below, first combinations of aspects andembodiments are articulated in a shorthand form such that (1) accordingto respective embodiments, for each instance in which a “component” orother such identifiers appear to be introduced (e.g., with “a” or “an,”)more than once in a given chain of clauses, such designations may eitheridentify the same entity or distinct entities; and (2) what might becalled “dependent” clauses below may or may not incorporate, inrespective embodiments, the features of “independent” clauses to whichthey refer or other features described above.

Clauses

1. A machine learning method for facilitating multi-parcel development(e.g., comprising one or more data flows 2500, 2600 or operational flows2100, 2200, 2300, 2400, 2700 described above) comprising:

invoking first transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to cause a first presentation 834 of afirst project site prioritization 833, 1933A favoring first-type andsecond-type project options 1584, 1984A-B (e.g. respectivelycorresponding to species 1024 and species 1025) over a third-typecomposite project option 1584, 1984 (e.g. corresponding to species 1022or species 1023) to be presented to a first device user 10D wherein thefirst presentation 834 corresponds (at least) to a rendered image 296depicting at least one geometric shape 1564 of at least one of thefirst-type composite project option 1584, 1984A or the second-typeproject option 1584, 1984B and wherein the first device user 10D isassociated with second and third device users 10A-B; and

invoking second transistor-based circuitry (e.g. one or more control andresponse modules 332, 336 jointly) configured to express a secondproject site prioritization 833, 1933B later favoring the third-typecomposite project option 1584, 1984 over the first-type and second-typeproject options 1584, 1984A-B to the first device user 10D partly basedon the second and third device users 10A-B having disparaged thefirst-type composite project option 1584, 1984A and partly based on anexplicit indication 840 (e.g. from a first indexing module 335) of apreference 466, 866 of the first device user 10D for the third-typecomposite project option 1584, 1984 whereby an expression 850 of thesecond project site prioritization 833, 1933B later favoring thethird-type composite project option 1584, 1984 over the first-type andsecond-type project options 1584, 1984A-B is obtained that comprises arendered image 296, 1096 depicting at least one geometric shape 1564 ofthe third-type composite project option 1584, 1984.

2. A machine learning method for facilitating multi-parcel development(e.g., comprising one or more data flows 2500, 2600 or operational flows2100, 2200, 2300, 2400, 2700 described above) comprising:

invoking transistor-based circuitry (e.g. one or more interface modules331) configured to cause a first presentation 834 of a first pluralityof project options 1284, 1584, 1984 on a first map 835 (e.g. in adepiction 297A-G that shows a geographic shape such as by latitude andlongitude) or ranked list 473 so that a position 1212 of a first option1284A, 1284D of the project options is more forward (e.g. “approaching alake” or “downhill” or toward some other latent or apparent userpreference) than a position of a second option 1284B (e.g. at minimum1301B) of the project options and so that a position of a third option1284C (e.g. at maximum 1302B) of the project options is more forwardthan the position of the first option 1284A, 1284D of the projectoptions; and

invoking transistor-based circuitry (e.g. one or more indexing modules335) configured to evaluate candidate positions using a scoring protocol476D that is optimized at a position 1303B further forward than a range1242 that includes the first and third options 1284A, 1284C by adistance 1343 substantially proportional to a magnitude of a forward netchange 2044 (i.e. within a factor of 2) corresponding to a firstpositional distribution 2050 of project options 1284 to a later secondpositional distribution 2050 of project options 1284 conditionally inresponse 1525 to a first indication 840 of said forward net change 2044.

3. A machine learning method for facilitating multi-parcel development(e.g., comprising one or more data flows 2500, 2600 or operational flows2100, 2200, 2300, 2400, 2700 described above) comprising:

invoking transistor-based circuitry (e.g. one or more interface modules331) configured to cause a first presentation 834 of a first pluralityof project options 1284, 1584, 1984 on a first map 835 (e.g. in adepiction 297A-G that shows a geographic shape such as by latitude andlongitude) or ranked list 473 so that a position 1212 of a first option1284A, 1284D of the project options is more forward (e.g. toward anuser-signaled path 1089, range 1242, or item or in some other direction2012 that a current user might generally favor) than a position of asecond option 1284B (e.g. at minimum 1301B) of the project options andso that a position of a third option 1284C (e.g. at maximum 1302B) ofthe project options is more forward than the position of the firstoption 1284A, 1284D of the project options; and

invoking transistor-based circuitry (e.g. one or more indexing modules335) configured to evaluate candidate positions using a scoring protocol476D that is optimized at a position 1303B offset further forward than arange 1242 that includes the first and third options 1284A, 1284C by adistance 1343 within an order of magnitude of a forward net change 2044(i.e. within a factor of 10) from a first geographic or other positionaldistribution 2050 of project options 1284 to a later second positionaldistribution 2050 of project options 1284 conditionally in response 1525to a first indication 840 of said forward net change 2044.

4. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more speciation modules333) configured to develop a highest-scoring one of the candidatepositions 1212 through numerous iterations 1424 of refinement speciation975 using the scoring protocol 476D conditionally in response 1525 tothe first indication 840 of the forward net change 2044 from the firstpositional distribution 2050 of earlier-favored project options 1284 tothe later second positional distribution 2050 of project options1284C-D, wherein (at least) an addition of the third option 1284Ccontributes positively to the forward net change 2044 (e.g. to a user's“favorites” list 473 or other favored set of species, options, orproject sites of an earlier distribution so that a later version of thedistribution 2050 it is treated as favored).

5. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more speciation modules333) configured to develop a highest-scoring one of the candidatepositions 1212 through very numerous iterations 1424 of refinementspeciation 975 using the scoring protocol 476D automatically andconditionally in response 1525 to the first indication 840 of theforward net change 2044 from the first positional distribution 2050 ofearlier-favored project options 1284 to the later second positionaldistribution 2050 of project options 1284C-D, wherein an addition of thethird option 1284C and a removal/omission 778 of the second option 1284B(e.g. from a user's “favorites” list 473 or other favored set ofspecies, options, or project sites of an earlier distribution so that alater version of the distribution 2050 it is not treated as favored)each contributes positively to the forward net change 2044.

6. A machine learning method for facilitating multi-parcel development(e.g., comprising one or more data flows 2500, 2600 or operational flows2100, 2200, 2300, 2400, 2700 described above) comprising:

invoking first transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to cause a first presentation 834 of afirst project site prioritization 833, 1933A favoring first-type andsecond-type project options 1584, 1984A-B (e.g. respectivelycorresponding to species 1024 and species 1025) over a third-typecomposite project option 1584, 1984 (e.g. corresponding to species 1022or species 1023) to be presented to a first device user 10D wherein thefirst presentation 834 corresponds (at least) to a rendered image 296depicting at least one geometric shape 1564 of at least one of thefirst-type composite project option 1584, 1984A or the second-typeproject option 1584, 1984B; and

invoking second transistor-based circuitry (e.g. one or more control andresponse modules 332, 336 jointly) configured to express a secondproject site prioritization 833, 1933B later favoring the third-typecomposite project option 1584, 1984 over the first-type and second-typeproject options 1584, 1984A-B to the first device user 10D partly basedon one or more indications 840 (e.g. from a first indexing module 335)signaling that the first device user 10D (apparently) has a geographicpreference 466, 866 for a vicinity 600 of the third-type compositeproject option 1584, 1984 over a vicinity of the first-type compositeproject option 1584 (e.g. favoring options in one district of the thirdtype over those in another or favoring an area 779 on one side of ageographic boundary 1918 instead of an opposite side that contains thefirst-type composite project option 1584, 1984) and partly based on theone or more indications 840 (e.g. from a second indexing module 335)signaling that the first device user 10D is (apparently or actually)more concerned about the third-type composite project option 1584, 1984than about the second-type composite project option 1584, 1984 wherebyan expression 850 of the second project site prioritization 833, 1933Blater favoring the third-type composite project option 1584, 1984 overthe first-type and second-type project options 1584, 1984A-B signalingthe second project site prioritization 833, 1933B that comprises arendered image 296, 1096 depicting at least one geometric shape 1564 ofthe third-type composite project option 1584, 1984 is thereby presentedto the first device user 10D.

7. A machine learning method for facilitating multi-parcel development(e.g., comprising one or more data flows 2500, 2600, 2700 or operationalflows 2100, 2200, 2300, 2400 described above) comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to cause a depiction 297 of anaugmented first species 201A of a first composite project site 123A thatcombines a first-type subject parcel 161A with a second-type primaryparcel 162A; and

invoking transistor-based circuitry (e.g. one or more instances ofauthorization modules 334) configured to respond to a first user request851 in reference to the second-type primary parcel 162A (e.g. clicking a“show owner” button or other such action 1594 indicative of a desire todisplay such metadata) by presenting an indication 840 of one or moresecond-type parcels 162 that include a second-type alternative parcel162B wherein responding to the first user request 851 in reference tothe second-type primary parcel 162A comprises invoking transistor-basedcircuitry (e.g. one or more instances of speciation modules 333)configured to develop (at least) an augmented (version 862 of a) species201B of a second composite project site 123B that combines a virtualbuilding model 202C spanning (at least) a third-type associated parcel163B with the second-type alternative parcel 162B and wherein thefirst-type, second-type, and third-type parcels are all mutuallyexclusive (i.e. wherein no first-type parcel overlaps a second- orthird-type parcel and no second-type parcel overlaps a third-typeparcel).

8. The machine learning method of any of the above methods wherein oneor more virtual building models 202 span two or more land parcels160-164 of a composite project site 123A-B through numerous iterations1424 of refinement speciation 975 as a conditional response 1525 to aproject site prioritization 833, 1933.

9. The machine learning method of any of the above methods wherein oneor more virtual building models 202 span two or more land parcels160-164 of a composite project site 123A-B through very numerousiterations 1424 of refinement speciation 975 as a conditional response1525 to a selective user action 1594 (e.g. a parcel selection).

10. The machine learning method of any of the above methods wherein oneor more first-type parcels 161 including a first-type subject parcel161A (thereof are included and) are each owned by a first entity 610Awho does not own a second-type primary parcel 162A (thereof that is alsoincluded) and that is owned by a second entity 610B and wherein themethod comprises:

invoking transistor-based circuitry (e.g. one or more instances ofcontrol modules 332) configured to present a depiction 297B of anaugmented species 201B of a second composite project site 123B thatcombines at least the second-type alternative parcel 162B with athird-type associated parcel 163B as a (primary aspect of a) conditionalresponse 1525 to a particular user action 1594 upon an indication 840 ofone or more second-type parcels 162.

11. The machine learning method of any of the above methods wherein oneor more first-type parcels 161 including the first-type subject parcel161A are each owned by a first entity 610A who does not own thesecond-type primary parcel 162A and that are each owned by a secondentity 610B who does not own any of the first-type parcels and whereinthe method comprises:

invoking transistor-based circuitry (e.g. one or more instances ofcontrol modules 332) configured to present a depiction 297B of anaugmented species 201B of a second composite project site 123B thatcombines at least the second-type alternative parcel 162B with athird-type associated parcel 163B as a (primary aspect of a) conditionalresponse 1525 to a first user action 1594 upon (a control 255representing or otherwise relating to) the second composite project site123B.

12. The machine learning method of any of the above methods wherein afirst type 370, 670A is defined so that one or more first-type parcels161 (i.e. parcels of the first type 370, 670A) including the first-typesubject parcel 161A are 100% owned by a first entity 610A who does notown the second-type primary parcel 162A; wherein a second type 370, 670Bis defined so that second-type parcels 162 including the second-typealternative parcel 162B are 100% owned by a second entity 610B; andwherein one or more third-type parcels 163 including the third-typeassociated parcel 163B are 100% owned by a third entity 610.

13. The machine learning method of any of the above methods comprising:

generating a first draft message 1560 configured with both an indication840 of one or more second-type parcels 162 that include a second-typealternative parcel 162B and a routing element 1559 associated with asecond entity 610B who owns the one or more second-type parcels 162 as aconditional response 1525 to a second user action 1594 at a clientdevice 1700 of another user 10; and

transmitting a final version 862 of the message 1560 that includes atleast the indication 840 of one or more second-type parcels 162 thatinclude the second-type alternative parcel 162B to the second entity610B using the routing element 1559 wherein the final version 862 of themessage 1560 identifies the second-type alternative parcel 162B but doesnot identify the second-type primary parcel 162A

14. The machine learning method of any of the above methods comprising:

obtaining an augmented species 201B of a second composite project site123B so as to combine at least a second-type alternative parcel 162Bwith a third-type associated parcel 163B and also with a building model202 spanning two or more parcels 162B, 163B of the second compositeproject site 123B, wherein responding to a first user request 851 inreference to the second-type primary parcel 162A comprises:

invoking transistor-based circuitry (e.g. one or more instances ofresponse modules 336) configured to identify a third-type associatedparcel 163B owned by a third entity 610 as being suitable forcombination with a second-type alternative parcel 162B owned by a secondentity 610B and invoking transistor-based circuitry (e.g. one or moreinstances of speciation modules 333) configured to develop (at least) anaugmented species 201B of the second composite project site 123B thatcombines a virtual building model 202C spanning at least the third-typeassociated parcel 163B owned by the third entity 610 with the secondcomposite project site 123B both as a conditional response 1525 to asecond user action 1594, wherein developing the augmented first species201B of the second composite project site 123B comprises implementingone or more deterministically repeatable speciation protocols 476B uponthe second composite project site 123B using a firstmulti-parcel-site-specific seeding configuration 1572 with the firstuser action 1594.

15. The machine learning method of any of the above methods wherein ascoring protocol 476D that affects a prioritization 833, 1933 places apositive incremental value 853 on a defined user action 1594 at a zone786, label, or other component of a map-resident first marker 785, 1185.

16. The machine learning method of any of the above methods wherein ascoring protocol 476D as described herein places a lesser-but-positivevalue 853 upon an earlier action 1594 at the particular marker 785, 1185than upon said defined action 1594 at the particular marker 785, 1185.

17. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more control modules332) configured to add at least one option 1284 corresponding to thehighest-scoring one of the candidate positions 1212 to the first map asan eligible new development option 1284K.

18. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more indexing modules335) configured to signal a cardinal direction 2012 as “forward” amongdozens of azimuths or more as a conditional response 1525 to one or moreuser actions 1594 signaling an apparent or other user preference 466,866.

19. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more indexing modules335) configured to signal a down-size direction 2012 as or up-sizedirection 2012 as “forward” among several operating parameters 2047 as aconditional response 1525 to one or more user actions 1594 signaling afirst (apparent or other) user preference 466, 866.

20. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more indexing modules335) configured to signal a “forward” direction 2012 as a conditionalresponse 1525 to one or more user actions 1594 signaling a change 2044of a user's favored project type(s) 370.

21. The machine learning method of any of the above methods whereinestablishing a forward net change 2044 comprises:

obtaining a first positional distribution 2050 comprising a range 1242that includes several candidate species 1025 including a most-forwardcandidate species 1025 having a most-forward offset 2005E; and

obtaining a second positional distribution 2050 comprising the forwardnet change 2044 partly based on several offsets 2005 that eachcorrespond to a corresponding one of the several candidate species 1025and partly based on an addition/inclusion 788 of the most-rearwardoffset 2005H to the first positional distribution 2050 so that aforemost component of the second positional distribution 2050 advancesforward.

22. The machine learning method of any of the above methods whereinestablishing a forward net change 2044 comprises:

obtaining a first positional distribution 2050 comprising a range 1242that includes several candidate species 1025 including a most-rearwardcandidate species 1025 having a most-rearward offset 2005H; and

obtaining a second positional distribution 2050 comprising the forwardnet change 2044 partly based on several offsets 2005 that eachcorrespond to a corresponding one of the several candidate species 1025and partly based on user's deletion or other removal/omission 778 of themost-rearward offset 2005H from the first positional distribution 2050so that a rearmost component/portion of the second positionaldistribution 2050 shrinks forward.

23. The machine learning method of any of the above methods wherein aforward net change 2044 (has been detected and) comprises a combinationof a particular option 1284C (apparently or actually) becoming favoredvia user input 1708 (e.g. a favorable signal 703A from a first deviceuser 10D) at a first device 1700 and another particular option 1284Fbecoming unfavored (e.g. by obtaining an unfavorable signal 703B fromthe first device user 10D) at the first device 1700.

24. The machine learning method of any of the above methods comprising:

invoking fifth transistor-based circuitry (e.g. one or more transmissionmodules 338) configures to cause an expression 850 of thehighest-scoring one of the candidate positions 1212 by presenting arendered image 296 depicting at least one geometric building shape 1564spanning parcels of an eligible new development option 1284.

25. The machine learning method of any of the above methods wherein 100%of all third-type parcel(s) 163 are owned by a third entity 610including the third-type associated parcel 163B.

26. The machine learning method of any of the above methods wherein oneor more virtual building models 202 span two or more land parcels160-164 of the third-type composite project option 1584, 1984 of thesecond presentation 834 through numerous iterations 1424 of refinementspeciation 975 as a conditional response 1525 to the second project siteprioritization 833, 1933B.

27. The machine learning method of any of the above methods wherein thesecond and third device users 10A-B having disparaged the first-typecomposite project option 1584, 1984A 1584, 1984A is deemed an implicitindication of a preference 466, 866 of the first device user 10D for thethird-type composite project option 1584, 1984 over the first-typeproject option 1584, 1984A implied by the first device user 10D beingassociated with second and third device users 10A-B.

28. The machine learning method of any of the above methods wherein theexplicit indication 840 expresses a first (dismissal or other)unfavorable signal 703B pertaining to the second-type project option1584, 1984B as a preference 466, 866 of the first device user 10D forthe third-type composite project option 1584, 1984 over at least thesecond-type project option 1584, 1984B.

29. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofmachine learning modules 337) configured to improve a first inclusion788 spanning (a boundary between) the second-type parcel 162 and anotherparcel of a second-type site species 1022 through numerous iterations1424 of refinement speciation 975 as a conditional real-time response1525 to the second project site prioritization 833, 1933B; and

30. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry configured to improve a firstinclusion 788 spanning (a boundary between) the second-type parcel 162and another parcel of the second-type site species 1022 through numerousiterations 1424 of refinement speciation 975 as a conditional real-timeresponse 1525 to the second project site prioritization 833, 1933B; and

initiating a development 1400B (e.g. by an additional machine learningmodule 337) of an additional inclusion 788 spanning (at least oneboundary between) the first-type parcel 161 and another parcel of thefirst-type site species 1021 through numerous iterations 1424 ofspeciation 975 as a conditional real-time response 1525 to the secondproject site prioritization 833, 1933B.

31. The machine learning method of any of the above methods wherein another-type composite project option 1584, 1984 features a first-typesite species 1021 comprising a first-type parcel 161, the methodcomprising:

automatically initiating a development 1400B of an additional (instanceof an) inclusion 788 spanning (at least one boundary between) thefirst-type parcel 161 and another parcel of the first-type site species1021 through numerous iterations 1424 of speciation 975 as a conditionalreal-time response 1525 to the second project site prioritization 833,1933B.

32. The machine learning method of any of the above methods wherein thefirst device user 10D is associated a cohort of other device users 10A-Cby one or more geographic terms 1590 (e.g. postal codes 371 or placenames).

33. The machine learning method of any of the above methods wherein thefirst device user 10D is associated a cohort of other device users 10A-C(at least) by one or more natural language place names (e.g. identifiers448 of a neighborhood, county, or city 446)

34. The machine learning method of any of the above methods wherein thefirst device user 10D is associated a cohort of other device users 10A-Cby one or more selective terms 1590 (e.g. search terms, project types,or other categorical selections 2694) used by all members of the cohort.

35. The machine learning method of any of the above methods wherein another-type composite project option 1584, 1984 features a first-typesite species 1021 comprising a first-type parcel 161, the methodcomprising:

automatically initiating a development 1400B of an additional (instanceof an) inclusion 788 spanning (at least one boundary between) thefirst-type parcel 161 and another parcel of the first-type site species1021 through numerous iterations 1424 of speciation 975 as a conditionalresponse 1525 to a determination that a type 370, 670 of the other-typecomposite project option 1584, 1984 is preferred by the first deviceuser 10D relative to one or more types 670 of the first-type andsecond-type composite project options 1584, 1984A-B as a component 769of the second project site prioritization 833, 1933B.

36. The machine learning method of any of the above methods wherein another-type composite project option 1584, 1984 features a first-typesite species 1021 comprising a first-type parcel 161 and wherein thethird-type composite project option 1584, 1984C features a second-typesite species 1022 comprising a second-type parcel 162, the methodcomprising:

initiating a development 1400B of an additional (instance of an)inclusion 788 spanning (at least one boundary between) the first-typeparcel 161 and another parcel of the first-type site species 1021through numerous iterations 1424 of speciation 975 as a conditionalresponse 1525 to the second project site prioritization 833, 1933B; and

continuing a development 1400B of an improved inclusion 788 spanning (aboundary between) the second-type parcel 162 and another parcel of thesecond-type site species 1022 through very numerous iterations 1424(i.e. at least hundreds) of refinement speciation 975 as a conditionalresponse 1525 to the second project site prioritization 833, 1933B.

37. The machine learning method of any of the above methods wherein another-type composite project option 1584, 1984 features a first-typesite species 1021 comprising a first-type parcel 161 and wherein thethird-type composite project option 1584, 1984C features a second-typesite species 1022 comprising a second-type parcel 162, the methodcomprising:

automatically initiating a development 1400B of an additional (instanceof an) inclusion 788 spanning (at least one boundary between) thefirst-type parcel 161 and another parcel of the first-type site species1021 through numerous iterations 1424 of speciation 975 as a conditionalresponse 1525 to a determination that a type 370, 670 of the other-typecomposite project option 1584, 1984 is (i.e. at least hundreds preferredby the first device user 10D relative to one or more types of thefirst-type and second-type composite project options 1584, 1984A-B as acomponent 769 of the second project site prioritization 833, 1933B; and

automatically continuing a development 1400B of an improved inclusion788 spanning (a boundary between) the second-type parcel 162 and anotherparcel of the second-type site species 1022 through very numerousiterations 1424 refinement speciation 975 as a conditional response 1525to a determination that a type of the third-type composite projectoption 1584, 1984C is (apparently or actually) preferred by the firstdevice user 10D relative to one or more types of the first-type andsecond-type composite project options 1584, 1984A-B as a component 769of the second project site prioritization 833, 1933B.

38. The machine learning method of any of the above methods whereby anexpression 850 of the second project site prioritization 833, 1933Blater favoring the third-type project option 1584, 1984C over thefirst-type and second-type project options 1584, 1984A-B is obtainedthat comprises a geographically mapped presentation 834 that correspondsto an updated rendered depiction 297 (e.g. relative to a prior version862A thereof) of one or more virtual building models 202 that span twoor more land parcels 160-164 of a composite project site 121-123 of theupdated rendered depiction 297.

39. The machine learning method of any of the above methods wherein theimplicit indication 840 of the first device user 10D comprises anunfavorable (signal 703A or other) indication 840B, 840D relating to thefirst-type composite project option 1584, 1984A from at least one otherdevice user 10 prior to any indication 840 relating to the first-typecomposite project option 1584, 1984A from the first device user 10D.

40. The machine learning method of any of the above methods wherein theimplicit indication 840 of the first device user 10D comprises anunfavorable (signal 703A or other) indication 840B, 840D relating to thefirst-type composite project option 1584, 1984A from at least one otherdevice user 10 in lieu of any indication 840 relating to the first-typecomposite project option 1584, 1984A from the first device user 10D.

41. The machine learning method of any of the above methods wherein thefirst geographically mapped presentation 834 includes a rendered image296, 1096 (depicting at least one parcel or building shape 1564) of thesecond-type composite project option 1584, 1984B and wherein theexplicit indication 840 (e.g. from one or more indexing modules 335) ofthe first device user 10D comprises a first unfavorable signal 703Apresented simultaneously with the rendered image 296, 1096 (e.g.depicting at least one parcel or building shape 1564) of one or moreother-type composite project options 1584, 1984B, 1584, 1984D (e.g. as acontextually explicit disparaging action 1594 thereof).

42. The machine learning method of any of the above methods wherein thefirst geographically mapped presentation 834 includes a rendered image296, 1096 (depicting at least one parcel or building shape 1564) of thethird-type composite project option 1584, 1984C.

43. The machine learning method of any of the above methods wherein thefirst geographically mapped presentation 834 includes a building shape1564 of the first-type composite project option 1584, 1984A and whereinthe second geographically mapped presentation 834 includes a buildingshape 1564 of the third-type composite project option 1584, 1984C inlieu of the building shape 1564 of the first-type composite projectoption 1584, 1984A.

44. The machine learning method of any of the above methods wherein thefirst geographically mapped presentation 834 includes a parcel shape1564 of the first-type composite project option 1584, 1984A and whereinthe second geographically mapped presentation 834 includes a parcelshape 1564 of the third-type composite project option 1584, 1984C inlieu of the parcel shape 1564 of the first-type composite project option1584, 1984A.

45. The machine learning method of any of the above methods wherein eachof the expressions 850 of a project site prioritization 833, 1933Acomprises a geographically mapped presentation 834 that includes arendered depiction 297 of one or more virtual building models 202 thatspan two or more land parcels 160-164 not commonly owned.

46. The machine learning method of any of the above methods whereinexpressing a second project site prioritization 833, 1933B favoring thethird-type project option 1584, 1984C (e.g. corresponding to asecond-type species 1022) over the first-type and second-type projectoptions 1584, 1984A-B comprises:

automatically and conditionally developing an additional (instance of a)third-type project option 1584, 1984; and

modifying the geographically mapped presentation 834 to include anadditional (instance of a) species 1022 of the additional third-typeproject option 1584, 1984C.

47. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to associate the first user 10D atleast with the second and third device users 10 based on at least thesedevice users 10A-D all having disparaged at least the second-typeproject option 1584, 1984B and one or more other-type project options1584, 1984 (directly or otherwise) but not having disparaged at leastone other-type species 1026.

48. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to associate the first user 10D atleast with the second and third device users 10 based on at least thesedevice users 10A-D all having disparaged at least one other-type projectoption 1584, 1984 directly by dismissing or otherwise disparaging atleast one species 1025B that is of the same type as that one other-typeproject option 1584, 1984.

49. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to associate the first user 10D atleast with the second and third device users 10 based on at least thesedevice users 10A-D all having disparaged at least one other-type projectoption 1584, 1984 indirectly by navigating away from the one other-typeproject option 1584, 1984.

50. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to associate the first user 10D atleast with the second and third device users 10 based on at least thesedevice users 10A-D all having disparaged at least one other-type projectoption 1584, 1984 indirectly by using an interface map 835 for along-enough period (e.g. for a period 1592 longer than a programmatictime limit that is more than an hour) without ever expressing anyinterest in any other-type project option 1584, 1984 thereof.

51. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to associate the first user 10D atleast with the second and third device users 10 based on at least thesedevice users 10A-D all having disparaged at least one unwanted-typeproject option 1584, 1984 indirectly by choosing several favoriteoptions 1584, 1984 (e.g. as favorite species 1022, 1023 or inclusions788C-D thereof) of which none are of a corresponding unwanted type.

52. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to associate the first user 10D atleast with the second and third device users 10 based on the first,second, and third device users all having (selected or otherwise)expressed one or more (municipally defined or other) values 853 that“selectively” qualify all of the first-, second-, and third-type projectoptions 1584, 1984A-C for inclusion in the first project siteprioritization 833, 1933A wherein the qualification is selective insofarthat at least one other-type species 1026 is excluded.

53. The machine learning method of any of the above methods comprising:

invoking transistor-based circuitry (e.g. one or more instances ofinterface modules 331) configured to associate the first user 10D atleast with the second and third device users 10 based on the first,second, and third device users all having expressed a first land useclassification 838 as one or more (parcel-descriptive or other) zoningvalues 853A that selectively qualifies all of the first-, second-, andthird-type project options 1584, 1984A-C for inclusion in the firstproject site prioritization 833, 1933A (e.g. by excluding at least oneother-type project option 1584, 1984).

54. The machine learning method of any of the above methods wherein theimplicit indication 840 of the first device user 10D comprises a firstunfavorable signal 703A presented while the rendered image 296, 1096depicts at least one parcel shape 1564 of the first-type compositeproject option 1584, 1984A.

55. The machine learning method of any of the above methods wherein theimplicit indication 840 (e.g. from a first indexing module 335) of thefirst device user 10D comprises a first (“show less” control 225 beingactivated or other) unfavorable signal 703A presented simultaneouslywith the rendered image 296, 1096 depicting at least one building shape1564 of the first-type composite project option 1584, 1984A.

56. The machine learning method of any of the above methods wherein theimplicit indication 840 of the first device user 10D comprises a firstunfavorable signal 703A relating to the rendered image 296, 1096 of thefirst-type composite project option 1584, 1984A.

57. The machine learning method of any of the above methods whereinafter (1) transmitting an expression 850 of an association 854, 2654between the first project selection parameter 831A and the first deviceuser 10D and an expression 850 of an association 854, 2654 between thesecond project selection parameter 831B and the first device user 10Dboth to the first device user 10D and (2) receiving a (dismissal orother) unfavorable signal 703B of the expression of the association 854,2654 between the first project selection parameter 831A and the firstdevice user 10D (e.g. by a transmission module 338), transistor-basedcircuitry (e.g. one or more instances of machine learning modules 337)is triggered to cause a development of a fourth-type composite projectsite selectively (at least partly) based on the second project selectionparameter 831B (and not on the first project selection parameter 831A)without the first device user 10D yet having expressly confirmed orpositively selected the association 854, 2654 with the second projectselection parameter 831B.

58. The machine learning method of any of the above methods whereinafter (1) one or more modules 331-338 transmitting an expression 850 ofan association 854, 2654 between the first project selection parameter831A and the first device user 10D and an expression 850 of anassociation 854, 2654 between the second project selection parameter831B and the first device user 10D both to the first device user 10D and(2) the one or more modules 331-338 receiving a (deletion or other)unfavorable signal 703B of the expression 850 of the association 854,2654 between the first project selection parameter 831A and the firstdevice user 10D;

invoking transistor-based circuitry (e.g. one or more instances ofcontrol and machine learning modules 332, 337 jointly) configured tocause a development of a fourth-type composite project site selectively(at least partly) based on the second project selection parameter 831B(and not on the first project selection parameter 831A) without thefirst device user 10D ever having expressly confirmed or positivelyselected the association 854, 2654 with the second project selectionparameter 831B; and

invoking transistor-based circuitry (e.g. one or more instances ofindexing and transmission modules 335, 338 jointly) configured to causea geographically mapped presentation 834 of a second project siteprioritization 833, 1933B that prioritizes the third- and fourth-typecomposite project sites over the first-type and second-type compositeproject sites selectively (at least partly) based on the second projectselection parameter 831B (and not on the first project selectionparameter 831A) without the first device user 10D ever having (expresslyconfirmed or otherwise positively) selected the association 854, 2654with the second project selection parameter 831B.

59. The machine learning method of any of the above Clauses comprising:

obtaining an indication 840 of a subtle interest (e.g. one or morepreferences 466, 866) of the first user 10 based on a second-typeprimary parcel 162A being included in a composite project site 123Awithout the first user 10 having actuated any controls 225 in that daythat coincide with any second-type primary parcel 162.

60. The machine learning method of any of the above Clauses comprising:

obtaining an indication 840 of a subtle interest (e.g. one or morepreferences 466, 866) of the first user 10 based on a second-typeprimary parcel 162A being included in a composite project site 123Awithout the first user 10 first having actuated any controls 225 in thatday that expressly identified any entity 610B that (owns or otherwise)controls any second-type primary parcel 162.

61. The machine learning method of any of the above Clauses comprising:

obtaining an indication 840 of a subtle interest (e.g. one or morepreferences 466, 866) of the first user 10 based on a second-typeprimary parcel 162A being included in a composite project site 123Awithout the first user 10 having actuated any controls 225 that coincidewith any second-type primary parcel 162 or owner thereof; and respondingto the subtle interest of the first user 10 in a best second-typeprimary parcel 162A by determining a plurality of alternative parcels162 that have a same type 370, 670B with the second-type primary parcel162A and by obtaining an indication 840 of which of the plurality ofalternative parcels 162 is best according to a current scoring protocol476D.

62. The machine learning method of any of the above Clauses comprising:

obtaining an indication 840 of a subtle interest (e.g. one or morepreferences 466, 866) of the first user 10 based on a second-typeprimary parcel 162A being included in a composite project site 123Awithout the first user 10 having actuated any controls 225 that coincidewith any second-type primary parcel 162 or owner thereof;

responding to the subtle interest of the first user 10 in a bestsecond-type primary parcel 162A by determining a plurality ofalternative parcels 162 that have a same type 370, 670B with thesecond-type primary parcel 162A and by obtaining an indication 840 ofwhich of the plurality of alternative parcels 162 is best according to acurrent scoring protocol 476D; and

presenting to the first user 10 an indication 840 which of the pluralityof alternative parcels 162 is the best second-type primary parcel 162Aafter using a speciation protocol 476B so as to develop a building model202C that spans the best second-type primary parcel 162A with one ormore other parcels 163B of a corresponding project site 123B.

63. The machine learning method of any of the above Clauses comprising:

obtaining an indication 840 of a subtle interest (e.g. one or morepreferences 466, 866) of the first user 10 based on a second-typeprimary parcel 162A being included in a composite project site 123Awithout the first user 10 having actuated any controls 225 that coincidewith any second-type primary parcel 162 or owner thereof;

responding to the subtle interest of the first user 10 in a bestsecond-type primary parcel 162A by determining a plurality ofalternative parcels 162 that have a (same entity 610B or other) sametype 370, 670B with the second-type primary parcel 162A and by obtainingan indication 840 of which of the plurality of alternative parcels 162is best according to a current scoring protocol 476D; and

presenting to the first user 10 an indication 840 which of the pluralityof alternative parcels 162 is the best second-type primary parcel 162Aafter using a speciation protocol 476B so as to develop a building model202C that spans the best second-type primary parcel 162A with one ormore other parcels 163B of a corresponding project site 123B.

64. The machine learning method of any of the above Clauses wherein anindication 840 which of a plurality of alternative parcels 162 is thebest second-type primary parcel 162A includes a single graphical image296 that shows that the building model 202C spans both the bestsecond-type primary parcel 162A and one or more other parcels 163B (atleast).

65. The machine learning method of any of the above Clauses comprising:

generating a preliminary first draft message 1560 configured with bothan indication 840 of the one or more second-type parcels 162 thatinclude the second-type alternative parcel 162B and a routing element1559 (e.g. a mailing/email address 453) associated with a second entity610B who owns the one or more second-type parcels 162 as a conditionalresponse 1525 to a second user action 1594B at a client device 1700; and

transmitting a version of the message 1560 (e.g. after some refinementby way of human input) that includes at least the indication 840 of theone or more second-type parcels 162 that include the second-typealternative parcel 162B to the second entity 610B using the routingelement 1559.

66. The machine learning method of any of the above Clauses comprising:

transmitting to an entity that owns both a higher-ranked primary parcel162A and a lower-ranked alternative parcel 162B a message 1560 thatrefers to the lower-ranked alternative parcel 162B but does not identifythe second-type primary parcel 162A.

67. The machine learning method of any of the above Clauses comprising:

responding to an identification of a particular entity 610B that owns aprimary parcel 162A in a higher-ranked composite project site 123A bydetermining whether the particular entity 610B also owns an alternativeparcel 162B and by implementing a speciation protocol 476B upon one ormore lower-ranked composite project sites 123B that include thealternative parcel 162B; and

thereafter addressing and transmitting to the particular entity 610B amessage 1560 that refers to one or more components (e.g. an address orother indication of the alternative parcel 162B) of the lower-rankedcomposite project site 123B but that does not contain any(identification of or other) emphasis upon the higher-ranked compositeproject site 123A.

68. The machine learning method of any of the above Clauses comprising:

transmitting to a particular entity 610 that owns both a higher-rankedprimary parcel 162A and a lower-ranked alternative parcel 162B a message1560 that refers to a composite project site 123A that includes thelower-ranked alternative parcel 162B but does not refer (directly to thehigher-ranked primary parcel 162A or otherwise) to any composite projectsite 123B that includes the second-type primary parcel 162A.

69. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. a control module 332)configured to obtain (at least) an identification of first and secondproject sites 121-123 wherein the first project site 121, 123 includes afirst parcel 161 adjacent a particular parcel 160 in combination withthe particular parcel 160 and wherein each of the first and secondproject sites 121-123 is a parcel assemblage.

70. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry configured to obtain anidentification of first and second project sites 121, 122, 123 wherein afirst (actual or other) project site 121, 123 includes a first parcel161 adjacent a particular parcel 160 in combination with the particularparcel 160 and wherein the second project site 122, 123 likewiseincludes a second parcel 162 adjacent the particular parcel 160 incombination with the particular parcel 160.

71. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. one or more other instances ofcontrol modules 332) configured to obtain an identification of first andsecond project sites 121, 122 wherein the first project site 121includes a first parcel 161 adjacent a particular parcel 160 incombination with a particular parcel 160 and wherein the second projectsite 122 likewise includes a second parcel 162 adjacent the particularparcel 160 in combination with the particular parcel 160.

invoking transistor-based circuitry (e.g. one or more instances ofspeciation modules 333) configured to obtain first and second buildingmodels 202 of the first project site 121 each based on a respectiveapplication 1577 of first and second speciation protocols 476B to afirst multi-parcel-site-specific seeding configuration 1572 associatedwith the first project site 121.

72. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry configured to obtain anidentification of first and second project sites 121, 122 wherein thefirst project site 121 includes a first parcel 161 adjacent a particularparcel 160 in combination with the particular parcel 160 and wherein thesecond project site 122 likewise includes a second parcel 162 adjacentthe particular parcel 160 in combination with the particular parcel 160;and

invoking transistor-based circuitry configured to obtain first andsecond building models 202 of the first project site 121 each based on arespective application 1577 of first and second speciation protocols476B to a first multi-parcel-site-specific seeding configuration 1572associated with the first project site 121.

73. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. one or more instances ofcontrol modules 332) configured to obtain an identification of first andsecond project sites 121, 122 wherein a first project site 121 includesa first parcel 161 adjacent a particular parcel 160 in combination withthe particular parcel 160 and wherein the second project site 122likewise includes a second parcel 162 adjacent the particular parcel 160in combination with the particular parcel 160;

invoking transistor-based circuitry (e.g. one or more instances ofspeciation modules 333) configured to obtain first and second buildingmodels 202 of the first project site 121 each based on a respectiveapplication 1577 of first and second speciation protocols 476B to afirst multi-parcel-site-specific seeding configuration 1572 associatedwith the first project site 121; and

circuitry (e.g. another one or more instances of speciation modules 333)configured to obtain first and second building models 202 of the secondproject site 122 each based on a respective application 1577 of firstand second speciation protocols 476B to a firstmulti-parcel-site-specific seeding configuration 1572 associated (atleast) with the second project site 122.

74. The machine learning method of any of the above Clauses wherein oneor more first-type parcels 161 are each owned by a first entity 610A andwherein one or more second-type parcels 162 are each owned by a secondentity 610B.

75. The machine learning method of any of the above Clauses wherein oneor more first-type parcels 161 (thereof are included and) are each ownedby a first entity 610A; wherein one or more second-type parcels 162 areeach owned by a second entity 610B; and wherein one or more third-typeparcels 163 are each owned by a third entity 610.

76. The machine learning method of any of the above Clauses wherein themethod comprises one or more pattern matching protocols 476E that causea selection of an augmented first species 201A-C as described herein.

77. The machine learning method of any of the above Clauses wherein themethod comprises one or more feature augmentation protocols 476F thatcause a first species 201A-C to be augmented in numerous incrementalimprovements as described herein.

78. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. another one or more instancesof speciation modules 333) configured to obtain first and secondbuilding models 202 of the second composite project site 122 each basedon a respective application 1577 of first and second speciationprotocols 476B to a first multi-parcel-site-specific seedingconfiguration 1572 associated (at least) with the second compositeproject site 122.

79. The machine learning method of any of the above Clauses wherein afirst speciation protocol 476B comprises a multi-building modelalgorithm like that of Table 4 herein.

80. The machine learning method of any of the above Clauses wherein oneor more speciation protocols 476B comprises a single-shelter algorithmlike that of Table 3 herein.

81. The machine learning method of any of the above Clauses wherein thefirst and second speciation protocols 476B each comprise amulti-building model algorithm or single-shelter algorithm and whereinseeding 475 for such algorithms comprises a (set of coordinates 861,dimensions 368, or other) repeatable designation of the reference parcel160 together with a repeatable designation of at least one otherparcel(s) thereof.

82. The machine learning method of any of the above Clauses comprising:

triggering a supervised-learning-type protocol 476C that includespairing numerous vector-valued input objects (e.g. as operational data405) each to a corresponding output value 853A-B using one or moreuser-provided inductive biases (e.g. observed user actions 1594).

83. The machine learning method of any of the above Clauses comprising:

triggering a supervised learning protocol 476C that includes pairingfirst input data that includes (at least) a first composite project site121 with a corresponding indication 840 of user preference 866 (e.g.activating a control 1595 depicted in an image 296 of the firstcomposite project site 121).

84. The machine learning method of any of the above Clauses comprising:

triggering a supervised learning protocol 476C that includes pairingnumerous vector-valued input objects each to a corresponding desiredoutput value 853A-B (e.g. a valuation 380, score 481, selection, rank488, authorization, or other user-provided preference indication 840)using one or more user-provided inductive biases.

85. The machine learning method of any of the above Clauses comprising:

triggering a supervised learning protocol 476C that includes pairingnumerous vector-valued input objects each to a corresponding desiredoutput value 853A-B (e.g. a valuation 380, score 481, selection, rank488, authorization, or other user-provided preference indication 840)using one or more user-provided inductive biases.

86. The machine learning method of any of the above Clauses comprising:

triggering a feature augmentation protocol 476F that includes anapplication 1577 of first and second speciation protocols to a firstmulti-parcel-site-specific seeding configuration 1572 associated with afirst composite project site 121 as described herein.

87. The machine learning method of any of the above Clauses comprising:

extracting one or more pattern definition terms 1590 for use in apattern matching protocol 476E as user input.

88. The machine learning method of any of the above Clauses comprising:

triggering a pattern-matching-type protocol 476E that determines aselective first inclusion of a first composite project site 123 in ageographical map 835.

89. The machine learning method of any of the above Clauses comprising:

triggering a pattern matching protocol 476E that determines a selectivefirst inclusion of a first assemblage in a geographical map 835 of adevelopment site 261, neighborhood, city 446, or other region 311 (e.g.as an inventory of matching models 202 or composite project sites thatexcludes some others in the region 311 that were not matching).

90. The machine learning method of any of the above Clauses comprising:

triggering a pattern matching protocol 476E that determines a selectivefirst inclusion of a first assemblage and a selective exclusion of oneor more other composite project sites within 4 kilometers of a firstcomposite project site both (at least partly) based on an application1577 of first and second speciation protocols to the firstmulti-parcel-site-specific seeding configuration 1572 associated withthe first composite project site.

91. The machine learning method of any of the above Clauses comprising:

triggering a pattern matching protocol 476E that determines a selectivefirst inclusion of a first assemblage and a selective exclusion of oneor more other composite project sites within 4 kilometers of a firstcomposite project site both partly based on an application 1577 of firstand second (instances of) speciation protocols 476B to the firstmulti-parcel-site-specific seeding configuration 1572 associated withthe first composite project site 121 and an application 1577 of thefirst and second speciation protocols to the firstmulti-parcel-site-specific seeding configuration 1572 associated withone or more other composite project sites.

92. The machine learning method of any of the above Clauses comprising:

triggering a feature augmentation protocol 476F that includes obtainingan other (instance of a) building model by gleaning a user-providedinductive bias manifesting a first inferred (apparent user) preference866 for a first speciation protocol, generating an other building modelusing the first speciation protocol 476B in lieu of the secondspeciation protocol, and displaying the other building modelsimultaneously with the first building model 202 (e.g. by showing bothin a map 835 of a city that includes both composite project sitesthereof).

93. The machine learning method of any of the above Clauses wherein themethod combines a supervised learning protocol 476C with a patternmatching protocol 476E.

94. The machine learning method of any of the above Clauses wherein themethod combines a pattern matching protocol 476E with a featureaugmentation protocol 476F.

95. The machine learning method of any of the above Clauses wherein themethod combines a feature augmentation protocol 476F with a supervisedlearning protocol 476C.

96. The machine learning method of any of the above Clauses wherein acomparison 1579 among one or more records 314 signals that theparticular parcel 160 is not commonly owned with the first parcel 161 orwith the second parcel 162 (or with both).

97. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. one or more instances ofauthorization modules 334) configured to cause the first building model202 of the first composite project site 121 to be prioritized over thesecond building model 202 of the first composite project site 121 and tobe presented to a user of a visual display 1512 (e.g. a device user 10Cusing one or more display screens 1712) in lieu of the second buildingmodel 202 based on a programmatic scoring protocol 476D (e.g. amachine-learning-based score 481, rank 488, or other valuation 380);

98. The machine learning method of any of the above Clauses comprising:

causing numerous additional composite project sites 121 to be depictedall via a single display screen 1712 all within a one-hour period 1592wherein each of the additional composite project sites 121 links acorresponding parcel 160 to at least one corresponding adjacent parcel161 with which the corresponding parcel 160 is adjacent and wherein thecorresponding parcels 160 include the particular parcel 160.

99. The machine learning method of any of the above Clauses comprising:

causing a collection of numerous additional composite project sites 121to be depicted all via a single display screen 1712 all within aten-minute period 1592 wherein each of the additional composite projectsites 121 links a corresponding parcel 160 to at least one correspondingadjacent parcel 161 with which the corresponding parcel 160 is adjacentand wherein the corresponding parcels 160 include the particular parcel160.

100. The machine learning method of any of the above Clauses comprising:

causing a geographically dispersed collection of numerous additionalcomposite project sites 121 to be depicted all via a single displayscreen 1712 all within a ten-minute period 1592 wherein each of theadditional composite project sites 121 links a corresponding parcel 160to at least one corresponding adjacent parcel 161 with which thecorresponding parcel 160 is adjacent, wherein more than half of thenumerous additional composite project sites 121 are separated from theother additional composite project sites 121 by more than 100 meters,and wherein the corresponding parcels 160 include the particular parcel160.

101. The machine learning method of any of the above Clauses comprising:

causing a first digital resource 1591 to be offered simultaneously formany of the additional composite project sites 121 on a first-comefirst-served basis so that one or more options 1584 presented toentities 610 that own a majority of the additional composite projectsites 121.

102. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. another one or more instancesof speciation modules 333) configured to obtain first and secondbuilding models 202 of a third composite project site 123 based on anapplication 1577 of the one or more other speciation protocols 476B to afirst multi-parcel-site-specific seeding configuration 1572 associatedwith the third composite project site 123; and

invoking transistor-based circuitry (e.g. one or more instances ofauthorization modules 334) configured to cause the first building model202 of the third composite project site 123 to be prioritized over theone or more building models 202 of the first and second compositeproject sites 121, 122 and to be signaled to the user 10 of the visualdisplay 1512 (at least partly) based on a programmatic scoring protocol476D.

103. The machine learning method of any of the above Clauses wherein theone or more records 314 signal that the particular parcel 160 is notcommonly owned with the first parcel 161 and wherein the one or morerecords 314 signal that the particular parcel 160 is not commonly ownedwith the second parcel 162.

104. The machine learning method of any of the above Clauses wherein theone or more records 314 signal that the particular parcel 160 is notcommonly owned with the first parcel 161 and wherein the one or morerecords 314 signal that the particular parcel 160 is not commonly ownedwith the second parcel 162 and wherein the first and second compositeproject sites 121, 122 are automatically included in an inventory as aconditional response 1525 to information in the one or more records 314indicating that at least the first and second parcels 161, 162 are notcommonly owned with one another.

105. The machine learning method of any of the above Clauses comprising:

causing the first building model of the second composite project site122 to be presented via the visual display 1512, 1712 in lieu of thesecond building model of the second composite project site 122 partlybased on the programmatic scoring protocol and partly based on one ormore preference-indicative actions 1594 of a user of the visual display1512, 1712 (e.g. as a contemporaneous direct or other response 1525 toan action of the user signaling interest in the particular parcel).

106. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. one or more instances ofauthorization modules 334) configured to cause the first building model202 of an other assemblage to be prioritized over (at least) the firstand second composite project sites 121, 122 and to displace the first orsecond composite project site 121, 122 partly based on the programmaticscoring protocol 476D and partly based on a first preference-indicativeaction 1594 of the user of the visual display 1512, 1712 (e.g. as acontemporaneous direct or other response 1525 to the firstpreference-indicative action 1594 of the user signaling interest in theparticular parcel).

107. The machine learning method of any of the above Clauses comprising:

invoking transistor-based circuitry (e.g. one or more instances ofauthorization modules 334) configured to cause the first building model202 of the first composite project site 121 to be prioritized over thesecond building model 202 of the first composite project site 121 and tobe presented to a user of a visual display (e.g. a device user 10C usingone or more display screens 1712) in lieu of the second building model202 based on a programmatic scoring protocol 476.

108. The machine learning method of any of the above Clauses comprising:

applying one or more deterministically repeatable speciation protocols476B as the applications 1577 of the first and second speciationprotocols 476B respectively to the first multi-parcel-site-specificseeding configuration 1572 associated with the first and secondcomposite project sites 121, 122.

109. The machine learning method of any of the above Clauses comprising:

applying one or more deterministically repeatable speciation protocols476B as the applications 1577 of the first and second speciationprotocols 476B respectively to the first multi-parcel-site-specificseeding configuration 1572 associated with the first and secondcomposite project sites 121, 122 by causing a recordation of one or moreparameters 831 (as operational data 405) thereof on a public blockchain455.

110. The machine learning method of any of the above Clauses comprising:

implementing the programmatic scoring protocol 476D to include adetermination of one or more machine-learning-based assemblagevaluations 380 as components of the programmatic scoring protocol 476.

111. The machine learning method of any of the above Clauses comprising:

implementing the programmatic scoring protocol 476D to include adetermination of one or more machine-learning-based scores 481 ascomponents of the programmatic scoring protocol 476.

112. The machine learning method of any of the above Clauses comprising:

implementing the programmatic scoring protocol 476D to include adetermination of one or more machine-learning-based ranks 488 ascomponents of the programmatic scoring protocol 476.

113. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included after having beenprovided by a user 10 of a client device 1700 thereof.

114. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore one or more timing restrictions 517 thereof.

115. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore one or more development-feature-specific restrictions 517 thereof(e.g. in regard to when a parcel thereof will become available).

116. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore geographic restrictions 517 thereof (e.g. in regard to where aproject site or parcel is).

117. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore species-type attributes 617 thereof (e.g. being a species ofresidential or hybrid project).

118. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore parcel-type-specific attributes 617 thereof (e.g. in regard to azoning or parcel-shape category).

119. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore site-type-specific attributes 617 thereof (e.g. in regard to a sitesize or relationship to a waterfront or other geographic boundary 1918).

120. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore parcel-owner-specific attributes 617 thereof (e.g. being awhitelisted or otherwise favored entity 610).

121. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included that comprises one ormore model-type-specific attributes thereof (e.g. featuring acommercial, industrial or hybrid model).

122. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof is included defining a vertical,areal, or other (approximate or other) physical dimension 368 thereof.

123. The machine learning method of any of the above Clauses wherein aproject-type attribute 617 thereof pertains to a scoring, suitability,or other artificial quantification 369 thereof.

124. The machine learning method of any of the above Clauses wherein afirst project type thereof is defined by combining two or moreproject-type attributes 617 as components thereof.

125. A machine learning system 300, 800, 1500 configured to perform anyof the above-described methods.

126. A machine learning system 300, 800, 1500 for facilitatingmulti-parcel development wherein the system implements one or more dataflows or operational flows (or both) as described herein.

127. A machine learning system 300, 800, 1500 configured to perform orotherwise facilitate most or all operations of flow 2200.

128. A machine learning system 300, 800, 1500 configured to perform orotherwise facilitate most or all operations of flow 2300.

129. A machine learning system 300, 800, 1500 configured to perform orotherwise facilitate most or all operations of flow 2400.

130. A machine learning system 300, 800, 1500 configured to perform orotherwise facilitate most or all operations of data flow 2500.

131. A machine learning system 300, 800, 1500 configured to perform orotherwise facilitate most or all operations of data flow 2600.

132. A machine learning system 300, 800, 1500 configured to perform orotherwise facilitate most or all operations of data flow 2700.

133. A machine learning system 300, 800, 1500 for facilitatingmulti-parcel development, the system comprising:

transistor-based circuitry (e.g. one or more instances of interfacemodules 331) configured to obtain an identification of (at least) firstand second composite project sites 121, 122 wherein the first compositeproject site 121 includes a first parcel 161 adjacent the particularparcel 160 in combination with the particular parcel 160, wherein thesecond composite project site 122 likewise includes a second parcel 162adjacent the particular parcel 160 in combination with the particularparcel 160, and wherein one or more records 314 signal that theparticular parcel 160 is not commonly owned with the first parcel 161 orwith the second parcel 162 (or with both);

transistor-based circuitry (e.g. one or more instances of speciationmodules 333) configured to obtain first and second building models 202of the first composite project site 121 each based on (at least) arespective application 1577 of first and second speciation protocols476B to a first multi-parcel-site-specific seeding configuration 1572associated with the first composite project site 121; and

transistor-based circuitry (e.g. another one or more instances ofspeciation modules 333) configured to obtain first and second buildingmodels 202 of the second composite project site 122 each based on arespective application 1577 of first and second speciation protocols476B (at least) to a first multi-parcel-site-specific seedingconfiguration 1572 associated with the second composite project site122.

134. A machine learning system 300, 400, 1500 for facilitatingmulti-parcel development, comprising:

transistor-based circuitry (e.g. one or more instances of interfacemodules 331) configured to cause a geographically mapped presentation834 of a first project site prioritization 833, 1933A favoringfirst-type and second-type project options 1584, 1984A-B (e.g.respectively corresponding to fourth-type species 1024 and fifth-typespecies 1025) over a third-type project option 1584, 1984 (e.g.corresponding to a second-type species 1022 or third-type species 1023)to a first device user 10D; and

transistor-based circuitry (e.g. one or more control and responsemodules 332, 336 jointly) configured to express a second project siteprioritization 833, 1933B later favoring the third-type project option1584, 1984 (e.g. corresponding to a second-type species 1022) over thefirst-type and second-type project options 1584, 1984A-B (e.g.respectively corresponding to fourth-type species 1024 and fifth-typespecies 1025) to the first device user 10D partly based on an explicitindication 840 (e.g. from a first indexing module 335) of a preference866 of the first device user 10D for the third-type composite projectoption 1584, 1984 over the second-type project option 1584, 1984B (e.g.from the first device user 10D having disparaged the second-typecomposite project option and partly based on an implicit indication 840of a preference 866 (e.g. from another indexing module 335) of the firstdevice user 10D for the third-type composite project option 1584, 1984over the first-type project option 1584, 1984A inferred from (at least)second and third device users 10A-B who have (dismissed or otherwise)disparaged the first-type composite project option 1584, 1984A.

135. Any machine learning system as described above, the systemcomprising:

transistor-based circuitry configured to improve a first inclusion 788spanning (a boundary between) the second-type parcel 162 and anotherparcel of the second-type site species 1022 through numerous iterations1424 of refinement speciation 975 as a conditional real-time response1525 to the second project site prioritization 833, 1933B.

136. Any machine learning system as described above, the systemcomprising:

transistor-based circuitry (e.g. one or more instances of machinelearning modules 337) configured to improve a first inclusion 788spanning (a boundary between) the second-type parcel 162 and anotherparcel of the second-type site species 1022 through numerous iterations1424 of refinement speciation 975 as a conditional real-time response1525 to the second project site prioritization 833, 1933B; and

transistor-based circuitry configured to implement a development 1400B(e.g. by an additional machine learning module 337) of an additionalinstance of an inclusion 788 spanning the first-type parcel 161 andanother parcel of the first-type site species 1021 through numerousiterations 1424 of speciation 975 as a conditional real-time response1525 to the second project site prioritization 833, 1933B.

137. Any machine learning system as described above wherein (at leasttwo of) the mentioned instances of transistor-based circuitry aregeographically remote from one another (i.e. more than 1 kilometerapart).

138. Any machine learning system as described above wherein all of thementioned instances of transistor-based circuitry reside within a singledevice (e.g. an ASIC).

139. Any machine learning system as described above including a firstindexing module 335 that comprises:

transistor-based circuitry 330 configured to manifest an activation of acontrol 1595 as a voltage configuration 355 to index to anext-most-preferable option, wherein such indexing modifies a userselection.

140. Any machine learning system as described above including a firstresponse module 336 that comprises:

transistor-based circuitry 330 configured to generate a conditionalresponse 1525 in which numerous parcels 160 in a region 311 eachundergoes one or more augmentations, disqualifications, or other suchdistillations (see FIG. 22).

141. Any machine learning system as described above including a firsttransmission module 338 that comprises:

transistor-based circuitry 330 configured to transmit one or moreinquiries 1583 or related resources 1591, optionally including a primaryaspect thereof sent to a cryptographically secured digital wallet (e.g.as security feature 1760) that receives or provides the one or moreresources 1591.

142. Any machine learning system as described above including a firsttransmission module 338 that comprises:

transistor-based circuitry 330 configured to transmit one or moreinquiries 1583 or related resources 1591, optionally including a primaryaspect thereof sent to one or more mining rigs that compriseproof-of-work blockchain node devices 1700.

143. Any machine learning system as described above including a firsttransmission module 338 that comprises:

transistor-based circuitry 330 configured to transmit one or moreinquiries 1583 or related resources 1591, optionally including a primaryaspect thereof sent to one or more stake authority nodes that compriseproof-of-stake blockchain node devices 1700.

144. Any machine learning system as described above including a firstcontrol module 332 that comprises:

transistor-based circuitry 330 configured to perform an instance of oneor more other modules by delegation (e.g. by triggering one or morefunctions thereof to be performed abroad or in one or more cloud servers1800).

With respect to the numbered claims expressed below, those skilled inthe art will appreciate that recited operations therein may generally beperformed in any order. Also, although various operational flows arepresented in sequence(s), it should be understood that the variousoperations may be performed in other orders than those which areillustrated or may be performed concurrently. Examples of such alternateorderings may include overlapping, interleaved, interrupted, reordered,incremental, preparatory, supplemental, simultaneous, reverse, or othervariant orderings, unless context dictates otherwise. Furthermore, termslike “responsive to,” “related to,” or other such transitive,relational, or other connections do not generally exclude such variants,unless context dictates otherwise.

1-14. (canceled)
 15. A feature augmentation and pattern matching methodfor facilitating multi-parcel development, comprising: invoking firsttransistor-based circuitry configured to cause a first presentation of afirst project site prioritization favoring first-type and second-typecomposite project options over a third-type composite project option tobe presented to a first device user wherein said first presentationcorresponds to a rendered image depicting at least one geometric shapeof said first-type composite project option and wherein said firstdevice user is associated with second and third device users; andautomatically invoking second transistor-based circuitry configured toexpress a second project site prioritization later favoring saidthird-type composite project option over said first-type and second-typecomposite project options to said first device user partly based on saidsecond and third device users having disparaged said first-typecomposite project option as an implicit indication of a preference ofsaid first device user for said third-type composite project option oversaid first-type composite project option implied by said first deviceuser being associated with second and third device users and partlybased on an explicit indication of a preference of said first deviceuser for said third-type composite project option over said second-typecomposite project option whereby an expression of said second projectsite prioritization later favoring said third-type composite projectoption over said first-type and second-type composite project options isobtained that comprises a rendered image depicting at least onegeometric shape of said third-type composite project option wherein oneor more updated virtual building models span two or more land parcels ofsaid third-type composite project option through very numerousiterations of refinement speciation as a conditional real-time responseto said second project site prioritization.
 16. The method of claim 15wherein said third-type composite project option features a first-typesite species comprising a first-type parcel owned by a first entity andwherein said third-type composite project option features a second-typesite species comprising a second-type parcel owned by a second entity.17. The method of claim 15 wherein said third-type composite projectoption features a first-type site species comprising a first-type parcelowned by a first entity, wherein said third-type composite projectoption features a second-type site species comprising a second-typeparcel owned by a second entity, wherein said implicit indication ofsaid preference of said first device user comprises an unfavorableindication relating to said first-type composite project option from atleast one other device user prior to any indication relating to saidfirst-type composite project option from said first device user, andwherein expressing said second project site prioritization favoring saidthird-type composite project option over said first-type and second-typecomposite project options comprises: modifying said first presentationto include a third-type site species.
 18. The method of claim 15 whereinafter transmitting an expression of an association between said firstproject selection parameter and said first device user and an expressionof an association between said second project selection parameter andsaid first device user both to said first device user and afterreceiving an unfavorable signal of said expression of said associationbetween said first project selection parameter and said first deviceuser, invoking third transistor-based circuitry causes a development ofa fourth-type composite project site selectively based on said secondproject selection parameter without said first device user yet havingexpressly confirmed or positively selected said association with saidsecond project selection parameter.
 19. The method of claim 15 whereinsaid first presentation includes a parcel shape of said first-typecomposite project option and wherein a second presentation includes aparcel shape of said third-type composite project option in lieu of saidparcel shape of said first-type composite project option.
 20. The methodof claim 15 wherein expressing said second project site prioritizationlater favoring said third-type composite project option over saidfirst-type and second-type composite project options comprises:performing numerous iterations of speciation upon digital content thatportrays said third-type composite project option after said explicitindication of said preference of said first device user for saidthird-type composite project option over said second-type compositeproject option whereby said rendered image of said third-type compositeproject option is refined; and later presenting said rendered image ofsaid third-type composite project option.
 21. The method of claim 15wherein expressing said second project site prioritization laterfavoring said third-type composite project option over said first-typeand second-type composite project options comprises: performing numerousiterations of speciation upon digital content that portrays saidthird-type composite project option after disparagement of saidfirst-type composite project option by at least said second device useras a primary aspect of said implicit indication of said preference ofsaid first device user for said third-type composite project option oversaid first-type composite project option whereby said rendered image ofsaid third-type composite project option is refined; and presenting saidrendered image of said third-type composite project option.
 22. Afeature augmentation and pattern matching method for facilitatingmulti-parcel development, comprising: invoking first transistor-basedcircuitry configured to cause a first presentation of a first projectsite prioritization favoring first-type and second-type compositeproject options over a third-type composite project option to bepresented to a first device user wherein said first presentationcorresponds to a rendered image depicting at least one geometric shapeof said first-type composite project option and wherein said firstdevice user is associated with second and third device users; andinvoking second transistor-based circuitry configured to express asecond project site prioritization later favoring said third-typecomposite project option over said first-type and second-type compositeproject options to said first device user partly based on said secondand third device users having disparaged said first-type compositeproject option and partly based on an explicit indication of apreference of said first device user for said third-type compositeproject option whereby an expression of said second project siteprioritization later favoring said third-type composite project optionover said first-type and second-type composite project options isobtained that comprises a rendered image depicting at least onegeometric shape of said third-type composite project option wherein oneor more virtual building models span two or more land parcels of saidthird-type composite project option through numerous iterations ofrefinement speciation as a conditional response to said second projectsite prioritization.
 23. The method of claim 22 whereby an expression ofsaid second project site prioritization later favoring said third-typecomposite project option over said first-type and second-type compositeproject options is obtained that comprises a geographically mappedpresentation that corresponds (at least) to an updated rendereddepiction of one or more virtual building models that each span two ormore land parcels of a composite project site of said updated rendereddepiction and wherein expressing said second project site prioritizationfavoring said third-type composite project option over said first-typeand second-type composite project options comprises: automaticallydeveloping an additional third-type species as a conditional responsebased on said implicit and explicit indications of said preference ofsaid first device user for said third-type composite project option; andconfiguring said geographically mapped presentation to include saidadditional third-type species.
 24. The method of claim 22 whereinanother-type composite project option features a first-type site speciescomprising a first-type parcel and wherein said third-type compositeproject option features a second-type site species comprising asecond-type parcel, said method comprising: automatically initiating adevelopment of an additional inclusion spanning said first-type parceland another parcel of said first-type site species through numerousiterations of speciation as a conditional real-time response to adetermination that a type of said other-type composite project option ispreferred by said first device user relative to one or more types ofsaid first-type and second-type composite project options as a primaryaspect of said second project site prioritization; and automaticallycontinuing a development of an improved inclusion spanning saidsecond-type parcel and another parcel of said second-type site speciesthrough very numerous iterations of refinement speciation as aconditional real-time response to a determination that a type of saidthird-type composite project option is preferred by said first deviceuser relative to one or more types of said first-type and second-typecomposite project options as a primary aspect of said second projectsite prioritization.
 25. The method of claim 22 wherein another-typecomposite project option features a first-type site species comprising afirst-type parcel and wherein said third-type composite project optionfeatures a second-type site species comprising a second-type parcel,said method comprising: initiating a development of an additionalinclusion spanning said first-type parcel and another parcel of saidfirst-type site species through numerous iterations of speciation as aconditional response to said second project site prioritization; andcontinuing a development of an improved inclusion spanning saidsecond-type parcel and another parcel of said second-type site speciesthrough very numerous iterations of refinement speciation as aconditional response to said second project site prioritization.
 26. Themethod of claim 22 wherein said invoking said first transistor-basedcircuitry configured to cause said first presentation of said firstproject site prioritization favoring said first-type and second-typecomposite project options over said third-type composite project optionto be presented to said first device user comprises: obtaining anunfavorable indication relating to said first-type composite projectoption from each of said second and third device users as an implicitindication of a preference of said first device user for said third-typecomposite project option over said first-type composite project optionimplied by said first device user being associated with at least secondand third device users.
 27. The method of claim 22 wherein said invokingsaid first transistor-based circuitry configured to cause said firstpresentation of said first project site prioritization favoring saidfirst-type and second-type composite project options over saidthird-type composite project option to be presented to said first deviceuser comprises: obtaining an unfavorable indication relating to saidfirst-type composite project option from each of said second and thirddevice users as an implicit indication of a preference of said firstdevice user for said third-type composite project option over saidfirst-type composite project option implied by said first device userbeing associated with at least second and third device users whereinsaid unfavorable indications were each presented simultaneously with arendered image of said first-type composite project option and wherein afirst geographically mapped presentation includes a rendered image ofsaid second-type composite project option.
 28. The method of claim 22wherein after one or more modules transmitting an expression of anassociation between said first project selection parameter and saidfirst device user and an expression of an association between saidsecond project selection parameter and said first device user both tosaid first device user and said one or more modules receiving aunfavorable signal of said expression of said association between saidfirst project selection parameter and said first device user: invokingtransistor-based circuitry configured to cause a development of afourth-type composite project site selectively based on said secondproject selection parameter without said first device user ever havingexpressly confirmed or positively selected said association with saidsecond project selection parameter; and invoking transistor-basedcircuitry configured to cause a geographically mapped presentation of asecond project site prioritization that prioritizes said third- andfourth-type composite project sites over said first-type and second-typecomposite project sites selectively based on said second projectselection parameter without said first device user ever having selectedsaid association with said second project selection parameter.
 29. Themethod of claim 22 wherein said first device user is associated withsaid second and third device users by one or more geographic terms madeby all of said device users and wherein an other-type composite projectoption features a first-type site species comprising a first-type parceland wherein said third-type composite project option features asecond-type site species comprising a second-type parcel, said methodcomprising: automatically initiating a development of an additionalinclusion spanning said first-type parcel and another parcel of saidfirst-type site species through numerous iterations of speciation as aconditional response to said second project site prioritization.
 30. Themethod of claim 22 wherein said first device user is associated withsaid second and third device users by one or more geographic terms madeby all of said device users and wherein said continuing said developmentof said improved inclusion comprises: initiating said continuing saiddevelopment of said improved inclusion as a conditional response to atype of said third-type composite project option being preferred by saidfirst device user relative to one or more types of said first-type andsecond-type composite project options as a primary aspect of said secondproject site prioritization.
 31. The method of claim 22 wherein saidfirst device user is associated with said second and third device usersby one or more geographic terms made by all of said device users andwherein an other-type composite project option features a first-typesite species comprising a first-type parcel and wherein said third-typecomposite project option features a second-type site species comprisinga second-type parcel, said method comprising: automatically initiating adevelopment of an additional inclusion spanning said first-type parceland another parcel of said first-type site species through numerousiterations of speciation as a conditional real-time response to anindication that a type of said other-type composite project option ispreferred by said first device user relative to one or more types ofsaid first-type and second-type composite project options as a primaryaspect of said second project site prioritization.
 32. A computerprogram product comprising: one or more tangible, nonvolatile storagemedia; and machine instructions borne on said one or more tangible,nonvolatile storage media which, when running on one or more computersystems, cause said one or more computer systems to perform said methodof claim
 22. 33. A feature augmentation and pattern matching system forfacilitating multi-parcel development, comprising: means for causing afirst presentation of a first project site prioritization favoringfirst-type and second-type composite project options over a third-typecomposite project option to be presented to a first device user whereinsaid first presentation corresponds to a rendered image depicting atleast one geometric shape of said first-type composite project optionand wherein said first device user is associated with second and thirddevice users; and means for expressing a second project siteprioritization later favoring said third-type composite project optionover said first-type and second-type composite project options to saidfirst device user partly based on said second and third device usershaving disparaged said first-type composite project option and partlybased on an explicit indication of a preference of said first deviceuser for said third-type composite project option whereby an expressionof said second project site prioritization later favoring saidthird-type composite project option over said first-type and second-typecomposite project options is obtained that comprises a rendered imagedepicting at least one geometric shape of said third-type compositeproject option wherein one or more virtual building models span two ormore land parcels of said third-type composite project option throughnumerous iterations of refinement speciation as a conditional responseto said second project site prioritization. 34-51. (canceled)